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How to tackle the global deforestation crisis

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Imagine if France, Germany, and Spain were completely blanketed in forests — and then all those trees were quickly chopped down. That’s nearly the amount of deforestation that occurred globally between 2001 and 2020, with profound consequences.

Deforestation is a major contributor to climate change, producing between 6 and 17 percent of global greenhouse gas emissions, according to a 2009 study. Meanwhile, because trees also absorb carbon dioxide, removing it from the atmosphere, they help keep the Earth cooler. And climate change aside, forests protect biodiversity.

“Climate change and biodiversity make this a global problem, not a local problem,” says MIT economist Ben Olken. “Deciding to cut down trees or not has huge implications for the world.”

But deforestation is often financially profitable, so it continues at a rapid rate. Researchers can now measure this trend closely: In the last quarter-century, satellite-based technology has led to a paradigm change in charting deforestation. New deforestation datasets, based on the Landsat satellites, for instance, track forest change since 2000 with resolution at 30 meters, while many other products now offer frequent imaging at close resolution.

“Part of this revolution in measurement is accuracy, and the other part is coverage,” says Clare Balboni, an assistant professor of economics at the London School of Economics (LSE). “On-site observation is very expensive and logistically challenging, and you’re talking about case studies. These satellite-based data sets just open up opportunities to see deforestation at scale, systematically, across the globe.”

Balboni and Olken have now helped write a new paper providing a road map for thinking about this crisis. The open-access article, “ The Economics of Tropical Deforestation ,” appears this month in the Annual Review of Economics . The co-authors are Balboni, a former MIT faculty member; Aaron Berman, a PhD candidate in MIT’s Department of Economics; Robin Burgess, an LSE professor; and Olken, MIT’s Jane Berkowitz Carlton and Dennis William Carlton Professor of Microeconomics. Balboni and Olken have also conducted primary research in this area, along with Burgess.

So, how can the world tackle deforestation? It starts with understanding the problem.

Replacing forests with farms

Several decades ago, some thinkers, including the famous MIT economist Paul Samuelson in the 1970s, built models to study forests as a renewable resource; Samuelson calculated the “maximum sustained yield” at which a forest could be cleared while being regrown. These frameworks were designed to think about tree farms or the U.S. national forest system, where a fraction of trees would be cut each year, and then new trees would be grown over time to take their place.

But deforestation today, particularly in tropical areas, often looks very different, and forest regeneration is not common.

Indeed, as Balboni and Olken emphasize, deforestation is now rampant partly because the profits from chopping down trees come not just from timber, but from replacing forests with agriculture. In Brazil, deforestation has increased along with agricultural prices; in Indonesia, clearing trees accelerated as the global price of palm oil went up, leading companies to replace forests with palm tree orchards.

All this tree-clearing creates a familiar situation: The globally shared costs of climate change from deforestation are “externalities,” as economists say, imposed on everyone else by the people removing forest land. It is akin to a company that pollutes into a river, affecting the water quality of residents.

“Economics has changed the way it thinks about this over the last 50 years, and two things are central,” Olken says. “The relevance of global externalities is very important, and the conceptualization of alternate land uses is very important.” This also means traditional forest-management guidance about regrowth is not enough. With the economic dynamics in mind, which policies might work, and why?

The search for solutions

As Balboni and Olken note, economists often recommend “Pigouvian” taxes (named after the British economist Arthur Pigou) in these cases, levied against people imposing externalities on others. And yet, it can be hard to identify who is doing the deforesting.

Instead of taxing people for clearing forests, governments can pay people to keep forests intact. The UN uses Payments for Environmental Services (PES) as part of its REDD+ (Reducing Emissions from Deforestation and forest Degradation) program. However, it is similarly tough to identify the optimal landowners to subsidize, and these payments may not match the quick cash-in of deforestation. A 2017 study in Uganda showed PES reduced deforestation somewhat; a 2022 study in Indonesia found no reduction; another 2022 study, in Brazil, showed again that some forest protection resulted.

“There’s mixed evidence from many of these [studies],” Balboni says. These policies, she notes, must reach people who would otherwise clear forests, and a key question is, “How can we assess their success compared to what would have happened anyway?”

Some places have tried cash transfer programs for larger populations. In Indonesia, a 2020 study found such subsidies reduced deforestation near villages by 30 percent. But in Mexico, a similar program meant more people could afford milk and meat, again creating demand for more agriculture and thus leading to more forest-clearing.

At this point, it might seem that laws simply banning deforestation in key areas would work best — indeed, about 16 percent of the world’s land overall is protected in some way. Yet the dynamics of protection are tricky. Even with protected areas in place, there is still “leakage” of deforestation into other regions. 

Still more approaches exist, including “nonstate agreements,” such as the Amazon Soy Moratorium in Brazil, in which grain traders pledged not to buy soy from deforested lands, and reduced deforestation without “leakage.”

Also, intriguingly, a 2008 policy change in the Brazilian Amazon made agricultural credit harder to obtain by requiring recipients to comply with environmental and land registration rules. The result? Deforestation dropped by up to 60 percent over nearly a decade. 

Politics and pulp

Overall, Balboni and Olken observe, beyond “externalities,” two major challenges exist. One, it is often unclear who holds property rights in forests. In these circumstances, deforestation seems to increase. Two, deforestation is subject to political battles.

For instance, as economist Bard Harstad of Stanford University has observed, environmental lobbying is asymmetric. Balboni and Olken write: “The conservationist lobby must pay the government in perpetuity … while the deforestation-oriented lobby need pay only once to deforest in the present.” And political instability leads to more deforestation because “the current administration places lower value on future conservation payments.”

Even so, national political measures can work. In the Amazon from 2001 to 2005, Brazilian deforestation rates were three to four times higher than on similar land across the border, but that imbalance vanished once the country passed conservation measures in 2006. However, deforestation ramped up again after a 2014 change in government. Looking at particular monitoring approaches, a study of Brazil’s satellite-based Real-Time System for Detection of Deforestation (DETER), launched in 2004, suggests that a 50 percent annual increase in its use in municipalities created a 25 percent reduction in deforestation from 2006 to 2016.

How precisely politics matters may depend on the context. In a 2021 paper, Balboni and Olken (with three colleagues) found that deforestation actually decreased around elections in Indonesia. Conversely, in Brazil, one study found that deforestation rates were 8 to 10 percent higher where mayors were running for re-election between 2002 and 2012, suggesting incumbents had deforestation industry support.

“The research there is aiming to understand what the political economy drivers are,” Olken says, “with the idea that if you understand those things, reform in those countries is more likely.”

Looking ahead, Balboni and Olken also suggest that new research estimating the value of intact forest land intact could influence public debates. And while many scholars have studied deforestation in Brazil and Indonesia, fewer have examined the Democratic Republic of Congo, another deforestation leader, and sub-Saharan Africa.

Deforestation is an ongoing crisis. But thanks to satellites and many recent studies, experts know vastly more about the problem than they did a decade or two ago, and with an economics toolkit, can evaluate the incentives and dynamics at play.

“To the extent that there’s ambuiguity across different contexts with different findings, part of the point of our review piece is to draw out common themes — the important considerations in determining which policy levers can [work] in different circumstances,” Balboni says. “That’s a fast-evolving area. We don’t have all the answers, but part of the process is bringing together growing evidence about [everything] that affects how successful those choices can be.”

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Deforestation and Forest Loss

Explore long-term changes in deforestation and deforestation rates across the world today., which countries are gaining, and which are losing forests.

Before we look specifically at trends in deforestation across the world, it's useful to understand the net change in forest cover. The net change in forest cover measures any gains in forest cover — either through natural forest expansion or afforestation through tree planting — minus deforestation.

This map shows the net change in forest cover across the world. Countries with a positive change (shown in green) are gaining forests faster than they're losing them. Countries with a negative change (shown in red) are losing more than they're able to restore.

A note on UN FAO forestry data

Data on net forest change, afforestation, and deforestation is sourced from the UN Food and Agriculture Organization's Forest Resources Assessment . Since year-to-year changes in forest cover can be volatile, the UN FAO provides this annual data averaged over five-year periods.

How much deforestation occurs each year?

Net forest loss is not the same as deforestation — it measures deforestation plus any gains in forest over a given period.

Between 2010 and 2020, the net loss in forests globally was 4.7 million hectares per year. 1 However, deforestation rates were much higher.

The UN FAO estimates that 10 million hectares of forest are cut down each year.

This interactive map shows deforestation rates across the world.

Read more about historical deforestation here:

The world has lost one-third of its forest, but an end of deforestation is possible

Over the last 10,000 years the world has lost one-third of its forests. An area twice the size of the United States. Half occurred in the last century.

Global deforestation peaked in the 1980s. Can we bring it to an end?

Since the end of the last ice age — 10,000 years ago — the world has lost one-third of its forests. 2 Two billion hectares of forest — an area twice the size of the United States — has been cleared to grow crops, raise livestock, and for use as fuelwood.

Previously, we looked at this change in global forests over the long run. What this showed was that although humans have been deforesting the planet for millennia, the rate of forest loss accelerated rapidly in the last few centuries. Half of the global forest loss occurred between 8,000 BCE and 1900; the other half was lost in the last century alone.

To understand this more recent loss of forest, let’s zoom in on the last 300 years. The world lost 1.5 billion hectares of forest over that period. That’s an area 1.5 times the size of the United States.

In the chart, we see the decadal losses and gains in global forest cover. On the horizontal axis, we have time, spanning from 1700 to 2020; on the vertical axis, we have the decadal change in forest cover. The taller the bar, the larger the change in forest area. This is measured in hectares; one hectare is equivalent to 10,000 m².

Forest loss measures the net change in forest cover: the loss in forests due to deforestation plus any increase in forest through afforestation or natural expansion. 3

Unfortunately, there is no single source that provides consistent and transparent data on deforestation rates over this period of time. Methodologies change over time, and estimates — especially in earlier periods — are highly uncertain. This means I’ve had to use two separate datasets to show this change over time. As we’ll see, they produce different estimates of deforestation for an overlapping decade — the 1980s — which suggests that these are not directly comparable. I do not recommend combining them into a single series, but the overall trends are still applicable and tell us an important story about deforestation over the last three centuries.

The first series of data comes from Williams (2006), who estimates deforestation rates from 1700 to 1995. 4 Due to poor data resolution, these are often given as average rates over longer periods — for example, annual average rates are given over the period from 1700 to 1849 and 1920 to 1949. That’s why these rates look strangely consistent over a long period of time.

The second series comes from the UN Food and Agriculture Organization (FAO). It produces a new assessment of global forests every five years. 5

The rate and location of forest loss changed a lot. From 1700 to 1850, 19 million hectares were being cleared every decade. That’s around half the size of Germany.

Most temperate forests across Europe and North America were being lost at this time. Population growth meant that today’s rich countries needed more and more resources such as land for agriculture, wood for energy, and construction. 6

Moving into the 20th century, there was a stepwise change in demand for agricultural land and energy from wood. Deforestation rates accelerated. This increase was mostly driven by tropical deforestation in countries across Asia and Latin America.

Global forest loss appears to have reached its peak in the 1980s. The two sources do not agree on the magnitude of this loss: Williams (2006) estimates a loss of 150 million hectares — an area half the size of India — during that decade.

Interestingly, the UN FAO 1990 report also estimated that deforestation in tropical ‘developing’ countries was 154 million hectares. However, it was estimated that the regrowth of forests offset some of these losses, leading to a net loss of 102 million hectares. 7

The latest UN Forest Resources Assessment estimates that the net loss in forests has declined in the last three decades, from 78 million hectares in the 1990s to 47 million hectares in the 2010s.

This data maps an expected pathway based on what we know from how human-forest interactions evolve.

As we explore in more detail later on , countries tend to follow a predictable development in forest cover, a U-shaped curve. 8 They lose forests as populations grow and demand for agricultural land and fuel increases, but eventually, they reach the so-called ‘forest transition point’ where they begin to regrow more forests than they lose.

That is what has happened in temperate regions: they have gone through a period of high deforestation rates before slowing and reversing this trend.

However, many countries — particularly in the tropics and sub-tropics — are still moving through this transition. Deforestation rates are still very high.

Deforestation rates are still high across the tropics

Large areas of forest are still being lost in the tropics today. This is particularly tragic because these are regions with the highest levels of biodiversity.

Let’s look at estimates of deforestation from the latest UN Forest report. This shows us raw deforestation rates without any adjustment for the regrowth or plantation of forests, which is arguably not as good for ecosystems or carbon storage.

This is shown in the chart below.

We can see that the UN does estimate that deforestation rates have fallen since the 1990s. However, there was very little progress from the 1990s to the 2000s and an estimated 26% drop in rates in the 2010s. In 2022, the FAO published a separate assessment based on remote sensing methods; it did not report data for the 1990s, but it also estimated a 29% reduction in deforestation rates from the early 2000s to the 2010s.

This is progress, but it needs to happen much faster. The world is still losing large amounts of primary forests every year. To put these numbers in context, during the 1990s and first decade of the 2000s, an area almost the size of India was deforested. 9 Even with the ‘improved’ rates in the 2010s, this still amounted to an area around twice the size of Spain. 10

The regrowth of forests is a positive development. In the chart below, we see how this affects the net change in global forests. Forest recovery and plantation ‘offsets’ a lot of deforestation such that the net losses are around half the rates of deforestation alone.

But we should be cautious here: it’s often not the case that the ‘positives’ of regrowing on planting one hectare of forest offset the ‘losses’ of one hectare of deforestation. Cutting down one hectare of rich tropical rainforest cannot be completely offset by the creation of on hectare of plantation forest in a temperate country.

Forest expansion is positive but does not negate the need to end deforestation.

The history of deforestation is a tragic one, in which we have lost not only wild and beautiful landscapes but also the wildlife within them. But, the fact that forest transitions are possible should give us confidence that a positive future is possible. Many countries have not only ended deforestation but have actually achieved substantial reforestation. It will be possible for our generation to achieve the same on a global scale and bring the 10,000-year history of forest loss to an end.

If we want to end deforestation, we need to understand where and why it’s happening, where countries are within their transition, and what can be done to accelerate their progress through it. We need to pass the transition point as soon as possible while minimizing the amount of forest we lose along the way.

In this article , I look at what drives deforestation, which helps us understand what we need to do to solve it.

Forest definitions and comparisons to other datasets

There is no universal definition of what a ‘forest’ is. That means there are a range of estimates of forest area and how this has changed over time.

In this article, in the recent period, I have used data from the UN’s Global Forest Resources Assessment (2020). The UN carries out these global forest stocktakes every five years. These forest figures are widely used in research, policy, and international targets, such as the Sustainable Development Goals .

The UN FAO has a very specific definition of a forest. It’s “land spanning more than 0.5 hectares with trees higher than 0.5 meters and a canopy cover of more than 10%, or trees able to reach these thresholds in situ.”

In other words, it has criteria for the area that must be covered (0.5 hectares), the minimum height of trees (0.5 meters), and a density of at least 10%.

Compare this to the UN Framework Convention on Climate Change (UNFCCC), which uses forest estimates to calculate land use carbon emissions, and its REDD+ Programme, where low-to-middle-income countries can receive finance for verified projects that prevent or reduce deforestation. It defines a forest as having a density of more than 10%, a minimum tree height of 2-5 meters, and a smaller area of at least 0.05 hectares.

It’s not just forest definitions that vary between sources. What is measured (and not measured) differs, too. Global Forest Watch is an interactive online dashboard that tracks ‘tree loss’ and ‘forest loss’ across the world. It measures this in real time and can provide better estimates of year-to-year variations in rates of tree loss.

However, the UN FAO and Global Forest Watch do not measure the same thing.

The UN FAO measures deforestation based on how land is used. It measures the permanent conversion of forested land to another use, such as pasture, croplands, or urbanization. Temporary changes in forest cover, such as losses through wildfire or small-scale shifting agriculture, are not included in deforestation figures because it is assumed that they will regrow. If the use of land has not changed, it is not considered deforestation.

Global Forest Watch (GFW) measures temporary changes in forests. It can detect changes in land cover but does not differentiate the underlying land use. All deforestation would be considered tree loss, but a lot of tree loss would not be considered as deforestation.

As GFW defines ‘forest loss’, “Loss” indicates the removal or mortality of tree cover and can be due to a variety of factors, including mechanical harvesting, fire, disease, or storm damage. As such, “loss” does not equate to deforestation.”

Therefore, we cannot directly compare these sources. This article from Global Forest Watch gives a good overview of the differences between the UN FAO's and GFW's methods.

Since GFW uses satellite imagery, its methods continually improve. This makes its ability to detect changes in forest cover even stronger. But it also means that comparisons over time are more difficult. It currently warns against comparing pre-2015 and post-2015 data since there was a significant methodological change at that time. Note that this is also a problem in UN FAO reports, as I’ll soon explain.

What data from GFW makes clear is that forest loss across the tropics is still very high, and in the last few years, little progress has been made. Since UN FAO reports are only published in 5-year intervals, they miss these shorter-term fluctuations in forest loss. The GFW’s shorter-interval stocktakes of how countries are doing will become increasingly valuable.

One final point to note is that UN FAO estimates have also changed over time, with improved methods and better access to data.

I looked at how net forest losses in the 1990s were reported across five UN reports: 2000, 2005, 2010, 2015, and 2020.

Estimated losses changed in each successive report:

• 2000 report : Net losses of 92 million hectares
• 2005 report : 89 million hectares
• 2010 report : 83 million hectares
• 2015 report : 72 million hectares
• 2020 report : 78 million hectares

This should not affect the overall trends reported in the latest report: the UN FAO should — as far as is possible — apply the same methodology to its 1990s, 2000s, and 2010s estimates. However, it does mean we should be cautious about comparing absolute magnitudes across different reports.

This is one challenge in presenting 1980 figures in the main visualization in this article. Later reports have not updated 1980 figures, so we have to rely on estimates from earlier reports. We don’t know whether 1980s losses would also be lower with the UN FAO’s most recent adjustments. If so, this would mean the reductions in net forest loss from the 1980s to 1990s were lower than is shown from available data.

Forest transitions: why do we lose then regain forests?

Globally, we deforest around ten million hectares of forest every year. 11 That’s an area the size of Portugal every year. Around half of this deforestation is offset by regrowing forests, so overall, we lose around five million hectares each year.

Nearly all — 95% — of this deforestation occurs in the tropics . But not all of it is to produce products for local markets. 14% of deforestation is driven by consumers in the world’s richest countries — we import beef, vegetable oils, cocoa, coffee, and paper that has been produced on deforested land. 12

The scale of deforestation today might give us little hope for protecting our diverse forests. But by studying how forests have changed over time, there’s good reason to think that a way forward is possible.

Many countries have lost and then regained forests over millennia.

Time and time again, we see examples of countries that have lost massive amounts of forests before reaching a turning point where deforestation not only slows but forests return. In the chart, we see historical reconstructions of country-level data on the share of land covered by forest (over decades, centuries, or even millennia, depending on the country). I have reconstructed long-term data using various studies, which I’ve documented here .

Many countries have much less forest today than they did in the past. Nearly half (47%) of France was forested 1000 years ago; today that’s just under one-third (31.4%). The same is true of the United States; back in 1630, 46% of the area of today’s USA was covered by forest. Today, that’s just 34%.

One thousand years ago, 20% of Scotland’s land was covered by forest. By the mid-18th century, only 4% of the country was forested. But then the trend turned, and it moved from deforestation to reforestation. For the last two centuries, forests have been growing and are almost back to where they were 1000 years ago. 13

Forest Transitions: the U-shaped curve of forest change

What’s surprising is how consistent the pattern of change is across so many countries; as we’ve seen, they all seem to follow a ‘U-shaped curve.’ They first lose lots of forest but reach a turning point and begin to regain it again.

We can illustrate this through the so-called ‘Forest Transition Model.’ 14 This is shown in the chart. It breaks the change in forests into four stages, explained by two variables: the amount of forest cover a region has and the annual change in cover (how quickly it is losing or gaining forest). 15

Stage 1 – The Pre-Transition phase is defined as having high levels of forest cover and no or only very slow losses over time. Countries may lose some forest each year, but this is at a very slow rate. Mather refers to an annual loss of less than 0.25% as a small loss.

Stage 2 – The Early Transition phase is when countries start to lose forests very rapidly. Forest cover falls quickly, and the annual loss of forest is high.

Stage 3 – The Late Transition phase is when deforestation rates start to slow down again. At this stage, countries are still losing forest each year, but at a lower rate than before. At the end of this stage, countries are approaching the ‘transition point.’

Stage 4 – The Post-Transition phase is when countries have passed the ‘transition point’ and are now gaining forest again. At the beginning of this phase, the forest area is at its lowest point. But forest cover increases through reforestation. The annual change is now positive.

Why do countries lose and then regain forests?

Many countries have followed this classic U-shaped pattern. What explains this?

There are two reasons that we cut down forests:

• Forest resources: we want the resources that they provide — the wood for fuel, building materials, or paper;
• Land: We want to use the land they occupy for something else, such as farmland to grow crops, pasture to raise livestock or land to build roads and cities.

Our demand for both of these initially increases as populations grow and poor people get richer . We need more fuelwood to cook, more houses to live in, and, importantly, more food to eat.

But, as countries continue to get richer, this demand slows. The rate of population growth tends to slow down. Instead of using wood for fuel, we switch to fossil fuels , or hopefully, more renewables and nuclear energy . Our crop yields improve, so we need less land for agriculture.

This demand for resources and land is not always driven by domestic markets. As I mentioned earlier, 14% of deforestation today is driven by consumers in rich countries.

The Forest Transition, therefore, tends to follow a ‘development’ pathway. 16 As a country achieves economic growth, it moves through each of the four stages. This explains the historical trends we see in countries across the world today. Rich countries — such as the USA, France, and the United Kingdom — have had a long history of deforestation but have now passed the transition point. Most deforestation today occurs in low-to-middle-income countries.

Where are countries in the transition today?

If we look at where countries are in their transition today, we can understand where we expect to lose and gain forest in the coming decades. Most of our future deforestation is going to come from countries in the pre-or early-transition phase.

Several studies have assessed the stage of countries across the world. 17 The most recent analysis to date was published by Florence Pendrill and colleagues (2019), which looked at each country’s stage in the transition, the drivers of deforestation, and the role of international trade. 18 To do this, they used the standard metrics discussed in our theory of forest transitions earlier: the share of land that is forested and the annual change in forest cover.

In the map, we see their assessment of each country’s stage in the transition. Most of today’s richest countries — all of Europe, North America, Japan, and South Korea — have passed the turning point and are now regaining forests. This is also true for major economies such as China and India. The fact that these countries have recently regained forests is also visible in the long-term forest trends above.

Across tropical and sub-tropical countries, we have a mix: many upper-middle-income countries are now in the late transition phase. Brazil, for example, went through a period of very rapid deforestation in the 1980s and 90s (its ‘early transition’ phase), but its losses have slowed, meaning it is now in the late transition. Countries such as Indonesia, Myanmar, and the Democratic Republic of Congo are in the early transition phase and are losing forests quickly. Some of the world’s poorest countries are still in the pre-transition phase. In the coming decades, we might expect to see the most rapid loss of forests unless these countries take action to prevent it and the world supports them in their goal.

Not all forest loss is equal: what is the difference between deforestation and forest degradation?

Fifteen billion trees are cut down every year. 19 The Global Forest Watch project — using satellite imagery — estimates that global tree loss in 2019 was 24 million hectares. That’s an area the size of the United Kingdom.

These are big numbers and important ones to track: forest loss creates a number of negative impacts, ranging from carbon emissions to species extinctions and biodiversity loss. But distilling changes to this single metric — tree or forest loss — comes with its own issues.

The problem is that it treats all forest loss as equal. It assumes the impact of clearing primary rainforest in the Amazon to produce soybeans is the same as logging plantation forests in the UK. The latter will experience short-term environmental impacts but will ultimately regrow. When we cut down primary rainforest, we transform this ecosystem forever.

When we treat these impacts equally, we make it difficult to prioritize our efforts in the fight against deforestation. Decision makers could give as much of our attention to European logging as to the destruction of the Amazon. As we will see later, this would be a distraction from our primary concern: ending tropical deforestation. The other issue that arises is that ‘tree loss’ or ‘forest loss’ data collected by satellite imagery often doesn’t match the official statistics reported by governments in their land use inventories. This is because the latter only captures deforestation — the replacement of forest with another land use (such as cropland). It doesn’t capture trees that are cut down in planted forests; the land is still forested; it’s now just regrowing forests.

In the article, we will look at the reasons we lose forests, how these can be differentiated in a useful way, and what this means for understanding our priorities in tackling forest loss.

Understanding and seeing the drivers of forest loss

‘Forest loss’ or ‘tree loss’ captures two fundamental impacts on forest cover: deforestation and forest degradation .

Deforestation is the complete removal of trees for the conversion of forest to another land use such as agriculture, mining, or towns and cities. It results in a permanent conversion of forest into an alternative land use. The trees are not expected to regrow . Forest degradation measures a thinning of the canopy — a reduction in the density of trees in the area — but without a change in land use. The changes to the forest are often temporary, and it’s expected that they will regrow.

From this understanding, we can define five reasons why we lose forests:

• Commodity-driven deforestation is the long-term, permanent conversion of forests to other land uses such as agriculture (including oil palm and cattle ranching), mining, or energy infrastructure.
• Urbanization is the long-term, permanent conversion of forests to towns, cities, and urban infrastructure such as roads.
• Shifting agriculture is the small- to medium-scale conversion of forest for farming, which is later abandoned so that forests regrow. This is common in local subsistence farming systems where populations will clear forest, use it to grow crops, and then move on to another plot of land.
• Forestry production is the logging of managed, planted forests for products such as timber, paper, and pulp. These forests are logged periodically and allowed to regrow.
• Wildfires destroy forests temporarily. When the land is not converted to a new use, forests can regrow in the following years.

Thanks to satellite imagery, we can get a birds-eye view of what these drivers look like from above. In the figure, we see visual examples from the study of forest loss classification by Philip Curtis et al. (2018), published in Science . 20

Commodity-driven deforestation and urbanization are deforestation : the forested land is completely cleared and converted into another land use — a farm, mining site, or city. The change is permanent. There is little forest left. Forestry production and wildfires usually result in forest degradation — the forest experiences short-term disturbance but, if left alone, is likely to regrow. The change is temporary. This is nearly always true of planted forests in temperate regions — there, planted forests are long-established and do not replace primary existing forests. In the tropics, some forestry production can be classified as deforestation when primary rainforests are cut down to make room for managed tree plantations. 18

'Shifting agriculture’ is usually classified as degradation because the land is often abandoned, and the forests regrow naturally. But it can bridge between deforestation and degradation depending on the timeframe and permanence of these agricultural practices.

One-quarter of forest loss comes from tropical deforestation

We’ve seen the five key drivers of forest loss. Let’s put some numbers on them.

In their analysis of global forest loss, Philip Curtis and colleagues used satellite images to assess where and why the world lost forests between 2001 and 2015. The breakdown of forest loss globally and by region is shown in the chart. 20

Just over one-quarter of global forest loss is driven by deforestation. The remaining 73% came from the three drivers of forest degradation: logging of forestry products from plantations (26%), shifting, local agriculture (24%), and wildfires (23%).

We see massive differences in how important each driver is across the world. 95% of the world’s deforestation occurs in the tropics [we look at this breakdown again later]. In Latin America and Southeast Asia, in particular, commodity-driven deforestation — mainly the clearance of forests to grow crops such as palm oil and soy and pasture for beef production — accounts for almost two-thirds of forest loss.

In contrast, most forest degradation — two-thirds of it — occurs in temperate countries. Centuries ago, it was mainly temperate regions that were driving global deforestation [we take a look at this longer history of deforestation in a related article ] . They cut down their forests and replaced them with agricultural land long ago. But this is no longer the case: forest loss across North America and Europe is now the result of harvesting forestry products from tree plantations or tree loss in wildfires.

Africa is also different here. Forests are mainly cut and burned to make space for local subsistence agriculture or fuelwood for energy. This ‘shifting agriculture’ category can be difficult to allocate between deforestation and degradation: it often requires close monitoring over time to understand how permanent these agricultural practices are.

Africa is also an outlier as a result of how many people still rely on wood as their primary energy source. Noriko Hosonuma et al. (2010) looked at the primary drivers of deforestation and degradation across tropical and subtropical countries specifically. 21  The breakdown of forest degradation drivers is shown in the following chart. Note that in this study, the category of subsistence agriculture was classified as a deforestation driver, so it is not included. In Latin America and Asia, the dominant driver of degradation was logging for products such as timber, paper, and pulp — this accounted for more than 70%. Across Africa, fuelwood and charcoal played a much larger role — it accounted for more than half (52%).

This highlights an important point: around one in five people in sub-Saharan Africa have access to clean fuels for cooking, meaning they still rely on wood and charcoal. With increasing development, urbanization, and access to other energy resources, Africa will shift from local subsistence activities into commercial commodity production — both in agricultural products and timber extraction. This follows the classic ‘forest transition’ model with development, which we look at in more detail in a related article .

Tropical deforestation should be our primary concern

The world loses almost six million hectares of forest each year to deforestation. That’s like losing an area the size of Portugal every two years. 95% of this occurs in the tropics. The breakdown of deforestation by region is shown in the chart. 59% occurs in Latin America, with a further 28% from Southeast Asia. In a related article , we look in much more detail at which agricultural products and which countries are driving this.

As we saw previously, this deforestation accounts for around one-quarter of global forest loss. 27% of forest loss results from ‘commodity-driven deforestation’ — cutting down forests to grow crops such as soy, palm oil, and cocoa, raising livestock on pasture, and mining operations. Urbanization, the other driver of deforestation, accounts for just 0.6%. It’s the foods and products we buy, not where we live, that have the biggest impact on global land use.

It might seem odd to argue that we should focus our efforts on tackling this quarter of forest loss (rather than the other 73%). But there is good reason to make this our primary concern.

Philipp Curtis and colleagues make this point clear. On their Global Forest Watch platform, they were already presenting maps of forest loss across the world. However, they wanted to contribute to a more informed discussion about where to focus forest conservation efforts by understanding why forests were being lost. To quote them, they wanted to prevent “a common misperception that any tree cover loss shown on the map represents deforestation.” And to “identify where deforestation is occurring; perhaps as important, show where forest loss is not deforestation.”

Why should we care most about tropical deforestation? There is a geographical argument (why the tropics?) and an argument for why deforestation is worse than degradation.

Tropical forests are home to some of the richest and most diverse ecosystems on the planet. Over half of the world’s species reside in tropical forests. 22 Endemic species are those which only naturally occur in a single country. Whether we look at the distribution of endemic mammal species , bird species , or amphibian species , the map is the same: tropical and subtropical countries are packed with unique wildlife. Habitat loss is the leading driver of global biodiversity loss. 23 When we cut down rainforests, we are destroying the habitats of many unique species and reshaping these ecosystems permanently. Tropical forests are also large carbon sinks and can store a lot of carbon per unit area. 24

Deforestation also results in larger losses of biodiversity and carbon relative to degradation. Degradation drivers, including logging and especially wildfires, can definitely have major impacts on forest health: animal populations decline, trees can die, and CO 2 is emitted. However, the magnitude of these impacts is often less than the complete conversion of forests. They are smaller and more temporary. When deforestation happens, almost all of the carbon stored in the trees and vegetation — called the ‘aboveground carbon loss’ —  is lost. Estimates vary, but on average, only 10-20% of carbon is lost during logging and 10-30% from fires. 25 In a study of logging practices in the Amazon and Congo, forests retained 76% of their carbon stocks shortly after logging. 26 Logged forests recover their carbon over time, as long as the land is not converted to other uses (which is what happens in the case of deforestation).

Deforestation tends to occur in forests that have been around for centuries if not millennia. Cutting them down disrupts or destroys established, species-rich ecosystems. The biodiversity of managed tree plantations, which are periodically cut, regrown, cut again, and then regrown, is not the same.

That is why we should be focusing on tropical deforestation. Since agriculture is responsible for 60 to 80% of it, what we eat, where it’s sourced from, and how it is produced are our strongest levers to bring deforestation to an end.

Carbon emissions from deforestation: are they driven by domestic demand or international trade?

95% of global deforestation occurs in the tropics. Brazil and Indonesia alone account for almost half. After long periods of forest clearance in the past, most of today’s richest countries are increasing tree cover through afforestation.

This might put the responsibility for ending deforestation solely on tropical countries. But, supply chains are international. What if this deforestation is being driven by consumers elsewhere?

Many consumers are concerned that their food choices are linked to deforestation in some of these hotspots. Since three-quarters of tropical deforestation is driven by agriculture, that’s a valid concern. It feeds into the popular idea that ‘eating local’ is one of the best ways to reduce your carbon footprint. In a previous article , I showed that the types of food you eat matter much more for your carbon footprint than where it comes from — this is because transport usually makes up a small percentage of your food’s emissions, even if it comes from the other side of the world. If you want to reduce your carbon footprint, reducing meat and dairy intake — particularly beef and lamb — has the largest impact.

But understanding the role of deforestation in the products we buy is important. If we can identify the producing and importing countries and the specific products responsible, we can direct our efforts towards interventions that will really make a difference.

Read more about the imported deforestation here:

Do rich countries import deforestation from overseas?

Rich countries import foods produced on deforested land in the tropics. How much deforestation do they import?

One-third of CO 2 emissions from deforestation are embedded in international trade

In a study published in Global Environmental Change , Florence Pendrill and colleagues investigated where tropical deforestation was occurring and what products were driving this. Using global trade models, they traced where these products were going in international supply chains. 27

They found that tropical deforestation — given as the annual average between 2010 and 2014 — was responsible for 2.6 billion tonnes of CO 2 per year. That was 6.5% of global CO 2 emissions. 28

International trade was responsible for around one-third (29%) of these emissions. This is probably less than many people would expect. Most emissions — 71% — came from foods consumed in the country where they were produced. It’s domestic demand, not international trade, that is the main driver of deforestation.

In the chart, we see how emissions from tropical deforestation are distributed through international supply chains. On the left-hand side, we have the countries (grouped by region) where deforestation occurs, and on the right, we have the countries and regions where these products are consumed. The paths between these end boxes indicate where emissions are being traded — the wider the bar, the more emissions are embedded in these products.

Latin America exports around 23% of its emissions; that means more than three-quarters are generated for products that are consumed within domestic markets. The Asia-Pacific region — predominantly Indonesia and Malaysia — exports a higher share: 44%. As we will see later, this is dominated by palm oil exports to Europe, China, India, North America, and the Middle East. Deforestation in Africa is mainly driven by local populations and markets; only 9% of its emissions are exported.

Since international demand is driving one-third of deforestation emissions, we have some opportunity to reduce emissions through global consumers and supply chains. However, most emissions are driven by domestic markets, which means that policies in major producer countries will be key to tackling this problem.

How much deforestation emissions is each country responsible for?

Let’s now focus on the consumers of products driving deforestation. After we adjust for imports and exports, how much CO 2 from deforestation is each country responsible for?

Rather than looking at total figures by country (if you’re interested, we have mapped them here ), we have calculated the per capita footprint. This gives us an indication of the impact of the average person’s diet. Note that this only measures the emissions from tropical deforestation — it doesn’t include any other emissions from agricultural production, such as methane from livestock or rice or the use of fertilizers.

In the chart, we see deforestation emissions per person, measured in tonnes of CO 2 per year. For example, the average German generated half a tonne (510 kilograms) of CO 2 per person from domestic and imported foods.

At the top of the list, we see Brazil and Indonesia, which are some of the major producer countries. The fact that the per capita emissions after trade are very high means that a lot of their food products are consumed by people in Brazil and Indonesia. The diet of the average Brazilian creates 2.7 tonnes of CO 2 from deforestation alone. That’s more than the country’s CO 2 emissions from fossil fuels , which are around 2.2 tonnes per person.

But we also see that some countries which import a lot of food have high emissions. Luxembourg has the largest footprint at nearly three tonnes per person. Imported emissions are also high for Taiwan, Belgium, and the Netherlands at around one tonne.

The average across the EU was 0.3 tonnes of CO 2 per person. To put this in perspective, that would be around one-sixth of the total carbon footprint of the average EU diet. 29

Beef, soybeans, and palm oil are the key drivers of deforestation

We know where deforestation emissions are occurring and where this demand is coming from. But we also need to know what products are driving this. This helps consumers understand what products they should be concerned about and allows us to target specific supply chains.

As we covered in a previous article , 60% of tropical deforestation is driven by beef, soybean, and palm oil production. We should look not only at where these foods are produced but also at where the consumer demand is coming from.

In the chart here, we see the breakdown of deforestation emissions by product for each consumer country. The default is shown for Brazil, but you can explore the data for a range of countries using the “Change country” button.

We see very clearly that the large Brazilian footprint is driven by its domestic demand for beef. In China, the biggest driver is demand for ‘oilseeds’ — which is the combination of soy imported from Latin America and palm oil imported from Indonesia and Malaysia.

Across the US and Europe, the breakdown of products is more varied. But, overall, oilseeds and beef tend to top the list for most countries.

Bringing all of these elements together, we can focus on a few points that should help us prioritize our efforts to end deforestation. Firstly, international trade does play a role in deforestation — it’s responsible for almost one-third of emissions. By combining our earlier Sankey diagram and breakdown of emissions by-product, we can see that we can tackle a large share of these emissions through only a few key trade flows. Most traded emissions are embedded in soy and palm oil exports to China and India, as well as beef, soy, and palm oil exports to Europe. The story of both soy and palm oil is complex — and it’s not obvious that eliminating these products will fix the problem. Therefore, we look at them both individually in more detail to better understand what we can do about it.

However, international markets alone cannot fix this problem. Most tropical deforestation is driven by the demand for products in domestic markets. Brazil’s emissions are high because Brazilians eat a lot of beef. Africa’s emissions are high because people are clearing forests to produce more food. This means interventions at the national level will be key: this can include a range of solutions, including policies such as Brazil’s soy moratorium, the REDD+ Programme to compensate for the opportunity costs of preserving these forests, and improvements in agricultural productivity so countries can continue to produce more food on less land.

FAO. 2020. Global Forest Resources Assessment 2020 – Key findings. Rome. https://doi.org/10.4060/ca8753en

Estimates vary, but most date the end of the last ice age to around 11,700 years ago.

Kump, L. R., Kasting, J. F., & Crane, R. G. (2004). The Earth System (Vol. 432). Upper Saddle River, NJ: Pearson Prentice Hall.

Year-to-year data on forest change comes with several issues: either data at this resolution is not available, or year-to-year changes can be highly variable. For this reason, data sources — including the UN Food and Agriculture Organization — tend to aggregate annual losses as the average over five-year or decadal periods.

Williams, M. (2003). Deforesting the earth: from prehistory to global crisis. University of Chicago Press.

The data for 1990 to 2020 is from the latest assessment: the UN’s Global Forest Resources Assessment 2020.

FAO (2020). Global Forest Resources Assessment 2020: Main report. Rome. https://doi.org/10.4060/ca9825en .

Mather, A. S., Fairbairn, J., & Needle, C. L. (1999). The course and drivers of the forest transition: the case of France. Journal of Rural Studies, 15(1), 65-90.

Mather, A. S., & Needle, C. L. (2000). The relationships of population and forest trends. Geographical Journal, 166(1), 2-13.

It estimated that the net change in forests without plantations was 121 million hectares. With plantations included — as is standard for the UN’s forest assessments — this was 102 million hectares.

Hosonuma, N., Herold, M., De Sy, V., De Fries, R. S., Brockhaus, M., Verchot, L., … & Romijn, E. (2012). An assessment of deforestation and forest degradation drivers in developing countries. Environmental Research Letters, 7(4), 044009.

The area of India is around 330 million hectares. The combined losses in the 1990s and 2000s were 309 million hectares. Just 6% less than the size of India.

The area of Spain is around 51 million hectares. Double this area is around 102 million hectares — a little under 110 million hectares.

The UN Food and Agriculture Organization (FAO) Forest Resources Assessment estimates global deforestation, averaged over the five-year period from 2015 to 2020, was 10 million hectares per year.

If we sum countries’ imported deforestation by World Bank income group , we find that high-income countries were responsible for 14% of imported deforestation; upper-middle-income for 52%; lower-middle income for 23%; and low income for 11%.

Mather, A. S. (2004). Forest transition theory and the reforesting of Scotland . Scottish Geographical Journal, 120(1-2), 83-98.

England is similar: in the late 11th century, 15% of the country was forested, and over the following centuries, two-thirds were cut down. By the 19th century, the forest area had been reduced to a third of what it once was. But it was then that England reached its transition point, and since then, forests have doubled in size.

National Inventory of Woodland and Trees, England (2001). Forestry Commission. Available here .

This was first coined by Alexander Mather in the 1990s. Mather, A. S. (1990). Global forest resources . Belhaven Press.

This diagram is adapted from the work of Hosonuma et al. (2012).

Hosonuma, N., Herold, M., De Sy, V., De Fries, R. S., Brockhaus, M., Verchot, L., ... & Romijn, E. (2012). An assessment of deforestation and forest degradation drivers in developing countries . Environmental Research Letters , 7 (4), 044009.

Rudel, T. K. (1998). Is there a forest transition? Deforestation, reforestation, and development . Rural Sociology , 63 (4), 533-552.

Rudel, T. K., Coomes, O. T., Moran, E., Achard, F., Angelsen, A., Xu, J., & Lambin, E. (2005). Forest transitions: towards a global understanding of land use change . Global Environmental Change , 15 (1), 23-31.

Cuaresma, J. C., Danylo, O., Fritz, S., McCallum, I., Obersteiner, M., See, L., & Walsh, B. (2017). Economic development and forest cover: evidence from satellite data . Scientific Reports , 7 , 40678.

Noriko Hosonuma et al. (2012) looked at this distribution for low-to-middle-income subtropical countries and also studied the many drivers of forest loss.Hosonuma, N., Herold, M., De Sy, V., De Fries, R. S., Brockhaus, M., Verchot, L., ... & Romijn, E. (2012). An assessment of deforestation and forest degradation drivers in developing countries . Environmental Research Letters , 7 (4), 044009.

Pendrill, F., Persson, U. M., Godar, J., & Kastner, T. (2019). Deforestation displaced: trade in forest-risk commodities and the prospects for a global forest transition . Environmental Research Letters , 14 (5), 055003.

Crowther, T. W., Glick, H. B., Covey, K. R., Bettigole, C., Maynard, D. S., Thomas, S. M., ... & Tuanmu, M. N. (2015). Mapping tree density at a global scale . Nature , 525 (7568), 201-205.

Curtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A., & Hansen, M. C. (2018). Classifying drivers of global forest loss . Science , 361 (6407), 1108-1111.

Hosonuma, N., Herold, M., De Sy, V., De Fries, R. S., Brockhaus, M., Verchot, L., ... & Romijn, E. (2012). An assessment of deforestation and forest degradation drivers in developing countries . Environmental Research Letters , 7(4), 044009.

Hosonuma et al. (2012) gathered this data from a range of sources, including country submissions as part of their REDD+ readiness activities, Center for International Forestry Research (CIFOR) country profiles, UNFCCC national communications, and scientific studies.

Scheffers, B. R., Joppa, L. N., Pimm, S. L., & Laurance, W. F. (2012). What we know and don’t know about Earth's missing biodiversity . Trends in Ecology & Evolution , 27(9), 501-510.

Maxwell, S. L., Fuller, R. A., Brooks, T. M., & Watson, J. E. (2016). Biodiversity: The ravages of guns, nets, and bulldozers . Nature, 536(7615), 143.

Lewis, S. L. (2006). Tropical forests and the changing earth system . Philosophical Transactions of the Royal Society B: Biological Sciences , 361(1465), 195-210.

Tyukavina, A., Hansen, M. C., Potapov, P. V., Stehman, S. V., Smith-Rodriguez, K., Okpa, C., & Aguilar, R. (2017). Types and rates of forest disturbance in Brazilian Legal Amazon, 2000–2013 . Science Advances , 3 (4), e1601047.

Lewis, S. L., Edwards, D. P., & Galbraith, D. (2015). Increasing human dominance of tropical forests . Science , 349 (6250), 827-832.

To do this, they quantified where deforestation was occurring due to the expansion of croplands, pasture, and tree plantations (for logging) and what commodities were produced on this converted land. Then, using a physical trade model across 191 countries and around 400 food and forestry products, they could trace them through to where they are physically consumed, either as food or in industrial processes.

Pendrill, F., Persson, U. M., Godar, J., Kastner, T., Moran, D., Schmidt, S., & Wood, R. (2019). Agricultural and forestry trade drives a large share of tropical deforestation emissions . Global Environmental Change , 56 , 1-10.

In 2012 — the mid-year of this period — global emissions from fossil fuels, industry, and land use change was 40.2 billion tonnes. Deforestation was therefore responsible for [2.6 / 40.2 * 100 = 6.5%].

The carbon footprint of diets across the EU varies from country to country, and estimates vary depending on how much land use change is factored into these figures. Notarnicola et al. (2017) estimate that the average EU diet, excluding deforestation, is responsible for 0.5 tonnes of CO 2 per person. If we add 0.3 tonnes to this figure, deforestation would account for around one-sixth [0.3 / (1.5+0.3) * 100 = 17%].

Notarnicola, B., Tassielli, G., Renzulli, P. A., Castellani, V., & Sala, S. (2017). Environmental impacts of food consumption in Europe . Journal of Cleaner Production , 140 , 753-765.

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• Published: 04 January 2024

Evaluating the impacts of a large-scale voluntary REDD+ project in Sierra Leone

• Mandy Malan 1 ,
• Rachel Carmenta   ORCID: orcid.org/0000-0001-8607-4147 2 ,
• Elisabeth Gsottbauer 3 , 4 ,
• Paul Hofman   ORCID: orcid.org/0000-0002-4091-876X 5 ,
• Andreas Kontoleon   ORCID: orcid.org/0000-0003-4769-898X 6 ,
• Tom Swinfield   ORCID: orcid.org/0000-0001-9354-5090 7 &
• Maarten Voors   ORCID: orcid.org/0000-0001-5907-3253 8  

Nature Sustainability volume  7 ,  pages 120–129 ( 2024 ) Cite this article

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Carbon offsets from the REDD+ (reducing emissions from deforestation and degradation) framework to protect forests are expected to see a 100-fold increase in market value by 2050. However, independent causal impact evaluations are scarce and only a few studies assess benefits to communities themselves, a core objective of REDD+. Following a pre-analysis plan, we use a before-after-control-intervention (BACI) framework to evaluate the impact of a large-scale voluntary REDD+ project in Sierra Leone—the Gola project. We use a panel of both satellite images and household surveys to provide causal evidence of the impact of the project on local deforestation rates and socioeconomic indicators over the first 5 yr of its implementation. We find that REDD+ slowed deforestation by 30% relative to control communities while not changing economic wellbeing and conservation attitudes. We find suggestive evidence that the programme increased the value of alternative income sources, by shifting labour away from forest-dependent farming activities. A cost-to-carbon calculation shows that REDD+ led to 340,000 tCO 2 in avoided emissions per year, with an estimated cost of US$1.12 per averted tCO 2 . Our study contributes to developing an evidence base for voluntary REDD+ projects and offers a robust approach to carry out BACI assessments.

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Voluntary carbon offset markets from projects in the tropics are expected to contribute to meeting net-zero climate change objectives in the Global North. In voluntary markets, certified third-party agencies sell carbon credits to buyers who aim to reduce their carbon footprint beyond levels legally required either by national domestic legislation or international commitments 1 .

Recently, there has been a surge in voluntary carbon credits, with the market value rising from US$473 million (all $ values in US dollars henceforth) in 2020 to edging close to $2 billion at the end of 2022 2 . The value of these offsets is expected to increase at least 100-fold by 2050, as industries and governments aim to meet the 1.5 °C Paris target 3 . In response to this surging demand, a new offset industry has emerged in which numerous entities develop carbon offset projects and seek their certification from private organizations through verification processes, after which myriad consultancy companies rate their quality and sell the offsets to buyers.

Voluntary carbon offsets can be sourced from different sectors and programmes, including forestry, which has coalesced around voluntary REDD+ projects. REDD+ (reducing emissions from deforestation and degradation) operates predominantly in tropical low-income countries with high rates of deforestation and forest degradation. REDD+ projects essentially provide incentives to (individual or communal) ‘owners’ of carbon-rich forests to reduce the baseline rate of deforestation and degradation, thereby increasing the storage of carbon (known as ‘additionality’). Incentives can be financial (cash subsidies) and/or in-kind such as goods or training, with varying degrees of enforced conditionality 4 , 5 . Hundreds of such voluntary REDD+ projects have been initiated globally and have received widespread attention on the basis of their ‘triple-win’ promise: the ability to reduce carbon emissions, improve livelihoods and conserve biodiversity 6 . However, 60% of REDD+ programmes are not certified to sell offsets in the voluntary carbon credit market, and the remaining is funded by grants 7 . The latter are very different types of programmes as they are typically not subject to the scrutiny of verification agencies or potential buyers.

Within the overall voluntary carbon credit market scene, REDD+ offsets have become the leading category, constituting 40% of the market. More importantly, in 2021 the market showed a 166% annual increase in the volume of traded carbon credits coming specifically from REDD+ projects that avoid unplanned deforestation and a 972% increase in programmes that avoid planned (legal) deforestation 8 , signalling the dynamism of forestry-based credits.

Despite this context of ‘REDD+ project-euphoria’, the scientific literature assessing the impacts of voluntary REDD+ offsets remains notably scant 9 . Carbon credit verification agencies do include monitoring and evaluation as part of their processes to renew voluntary carbon credits. However, the objectivity, transparency and robustness of these assessments have been called into question, undermining the credibility and viability of the voluntary offset market 10 . This controversy 11 has increased the calls within the scientific and policy communities for more independent and rigorous assessments of REDD+ projects 9 , 12 , 13 , 14 , 15 .

Two central aspects of voluntary REDD+ projects require independent evaluation: whether they secure carbon additionality (and avoid leakage or displaced deforestation) 16 , 17 and deliver benefits to local communities 18 . Many evaluations have relied largely on case studies that use data collected ‘after’ REDD+ projects commenced and/or without meaningful information from comparison sites 19 . Rigorous evaluations (that are purposefully designed to explore causal relationships as opposed to correlations) require the use of empirical methods that both compare locations that benefit from REDD+ projects with comparable control sites, as well as assess relevant data from the pre- and post-REDD+ project period. This enables researchers to undertake so called before-after-control-intervention (BACI) assessments 9 , 14 , 20 , 21 . Yet, these types of studies are remarkably sparse 4 .

Supplementary Information A and Supplementary Table 1 summarize published work that uses BACI approaches to assess the environmental effectiveness and livelihood impacts of REDD+ initiatives. The majority of studies focus on non-certified sub-national REDD+ initiatives, or projects that were in the early stages of development (pilot schemes) 18 , 20 , 21 , 22 , 23 , 24 , 25 , 26 . The overall picture that emerges from this work on mostly non-certified REDD+ initiatives is that they have had a rather muted impact on deforestation.

Remarkably, just four studies in our review, focus exclusively on actually certified REDD+ projects 10 , 27 , 28 , 29 . These studies focus on assessing deforestation impacts only and do not assess the livelihood impacts or behavioural mechanisms that could explain any changes in deforestation. They tend to rely on remote sensing data rather than field survey data. Where survey data are included, they tend to be ‘ex-post’ rather than collected ‘before and after’ in recipient/non-recipient control (that is, comparable) groups. A few studies find mildly positive or at least no negative impacts on welfare and livelihood indicators 21 , 23 , 30 .

In sum, the available body of evidence still leaves many under-researched issues concerning the environmental and economic impacts of voluntary carbon offset schemes. In this paper, we contribute to the need to build the evidence base by reporting on a study that evaluates the impacts of an actual certified voluntary REDD+ project. In particular, we contribute a BACI assessment of the impacts of such a project 5 yr after its commencement and consider the causal pathways through which REDD+ operates. Notably, we also add a cost-to-carbon analysis.

Our evaluation focuses on the REDD+ project surrounding the Gola Rainforest National Park (GRNP) in Sierra Leone, West Africa, established in 2011. Visual assessment of satellite images suggests that forest cover within the uninhabited GRNP has largely remained intact (Fig. 1 ). However, deforestation has increased in the area outside the park boundaries where regulatory prohibitions do not apply. This buffer zone (4 km wide) aims to protect the park from encroachment that may result from growing population pressure and serves as a corridor for species migration between the different park sections. In addition, the buffer zone is the expected area of leakage due to the imposed restrictions of logging within the park. The REDD+ project, is managed by the Gola Rainforest Conservation (GRC) and received certification in 2015 (although project activities started in 2014) by Verra, one of the largest carbon credit certification agencies. Its primary aim is to protect tropical forest inside the GRNP as well as in this buffer zone 31 . There are no large communities residing within the park; instead, the programme focuses on communities located within the buffer zone. The Gola REDD+ project was also designed to incentivize these buffer zone communities to shift away from traditional to more sustainable agricultural practices (such as forest-friendly crops such as cocoa) through a range of REDD+ activities including agricultural extension to increase yields, marketing support for securing better prices and access to (co-managed) financial services (see Supplementary Information C for a detailed description of the REDD+ programme and previous activities in the area). As is the case with many REDD+ projects 4 , the incentives for the specific scheme are only weakly conditional on conservation behaviour.

Each pixel shows whether any deforestation took place from 2001 until 2018. The dashed line shows the 4 km buffer zone in which the REDD+ programme took place. Source: ref. 42 .

We evaluate the short-run impact of REDD+ on deforestation rates, economic wellbeing and conservation attitudes within the park buffer zone for the first 5 yr of the project, spanning 2014–2018. We use satellite imagery to assess causal changes in deforestation rates and multiple rounds of detailed on-the-ground household-level survey data collected both before and after REDD+ activities began. The latter allows us to estimate the impacts on economic wellbeing and assess potential mechanisms behind any impacts of REDD+ on deforestation rates (see Supplementary Information D for data description). As the interventions were not randomly assigned to communities, our control group consists of comparable communities that lie within the chiefdoms of the park, but outside of the 4 km buffer zone. We conduct a simple cost-to-carbon calculation and compare the estimated cost per averted tonne of carbon to similar programmes globally.

Our study adds to the empirical work on evaluating REDD+ projects and makes several contributions. First, we undertook an analysis of an operational, fully developed, voluntary REDD+ project that actively sells carbon credits in voluntary markets and has been approved by a leading verification agency. As detailed in Supplementary Information A , most published impact evaluation studies on REDD+ projects focus on non-certified initiatives or pilots. Secondly, we established an agreement with the agencies developing the Gola REDD+ project to instate an independent BACI evaluation before the start of the project, which was conducted separately from the verification processes required by Verra. A rare feature of this design among similar studies is that we also made use of detailed baseline household survey collected before the commencement of the project (2014) and corresponding endline data collected more than 5 yr after the project began (2019). In addition, with access to a previous round of data (2010), we were able to explore parallel trends before the programme between project and control sites. Hence, our data allowed for a rigorous impact assessment, evaluating REDD+ impacts on economic wellbeing, deforestation rates, as well as the likely mechanisms that lead to any observed changes in the latter. Our analysis is limited to assessing the impacts of the REDD+ project within the buffer zone and does not include the GRNP itself because there is no credible counterfactual for the GRNP. Also, from a policy and REDD+ design point of view, deforestation pressure and leakage are likely to be larger in the buffer zone. Our analysis strategy is based on a pre-analysis plan that was submitted to an open-access repository before data were analysed (OSF id: 8n7h6). This aspect of our analysis contributes to the objective of improving transparency and credibility, which is increasingly called for and supported by researchers working in the environmental policy domain 32 , 33 , 34 . It is also essential for building the much-needed evidence base of independent and transparent studies on voluntary REDD+ projects.

Deforestation is a severe problem in Sierra Leone which, in the past two decades, has lost ~25% of its tree cover. This trend is mainly driven by small-scale traditional agriculture 35 . Pressure on protected areas (PAs) in the country is high. Figure 2a,b show forest loss rates within 8 PAs in Sierra Leone and their buffer zones for the 2001–2018 period. Average forest loss within PAs was ~1% yr −1 in the 2013–2018 period, lower than the national average of just under 3% (see Supplementary Fig. 1 for separate graphs for each PA). There is substantial pressure on PA buffer zones, with average deforestation rates of ~2.5% yr −1 between 2013 and 2018. Very similar trends were observed in neighbouring Guinea and Liberia (see Supplementary Fig. 2) , all pointing to the need for effective programmes that are able to reduce deforestation in sensitive areas such as PA buffer zones. It is against the backdrop of these worrying trends that we evaluate the impact of the REDD+ project implemented in the crucial buffer zone of the GRNP.

a , Total forest loss from 2001 to 2018 in 8 PAs of Sierra Leone and the average for Sierra Leone. b , Total forest loss from 2001 to 2018 in the 4-km buffer zones of these PAs. The PAs shown are the GRNP, Outamba, Loma Mountains, Western Area Peninsula, Kangari Hills, Tingi Hills, Kambui Hills and Tiwai Island (see Supplementary Fig. 1 for separate graphs for each PA). The break in the lines in 2013 denotes the launch of a new satellite (Landsat 8) resulting in more precise measures of forest loss. Source: ref. 42 /UMD/Google/USGS/NASA.

REDD+ impact on deforestation

To assess the impact of the REDD+ project, we focus on changes within REDD+ communities (that is, those in the buffer zone around the GRNP) and compare these to changes in communities adjacent (that is, 4–20 km) to the buffer zone, thereby keeping many factors constant (including population density, demography, land use and market access—all potential drivers of deforestation; see also Supplementary Table 4 for very similar levels of these drivers for REDD+ and non-REDD+ communities). We use data from the 454 communities surrounding the GRNP, of which 126 lie within the 4-km buffer zone and received REDD+ interventions. We examine forest loss for these REDD+ and the remaining 328 non-REDD+ communities over time (covering 2001–2018, with the REDD+ programme starting in 2014). On the basis of community location and population size, we use Voronoi polygons around each community to assign yearly forest loss rates (see Supplementary Information D for a detailed description of the data generation process). Yearly deforestation rates in both types of villages for the evaluation period 2001–2018 are shown in Fig. 3 . Visual inspection highlights that before the start of the REDD+ programme, deforestation rates in both groups trended very similarly. After the start of the programme, the percentage forest loss was significantly and substantially higher in non-REDD+ communities compared with REDD+ communities.

Total forest loss from 2001 to 2018 in REDD+ vs non-REDD+ villages. The village polygons were estimated using population-weighted Voronoi estimations. Data are presented as mean village-level values and shaded areas denote 95% confidence intervals. The vertical black line indicates the start of REDD+. The break in the lines in 2013 denotes the launch of a new satellite (Landsat 8) resulting in more precise measures of forest loss. Source: ref. 42 /UMD/Google/USGS/NASA.

To formally test the impact of REDD+ in the GRNP buffer zone, we use a difference-in-difference regression analysis to assess the change in trends over time (Table 1 ). We find that the REDD+ programme reduced (but not reversed) deforestation in the REDD+ communities by ~1 percentage point (or 30%) compared with non-REDD+ communities. Hence, while the programme reduced the amount of deforestation by ~929 ha yr −1 in the buffer zone, it did not remove pressure on forests completely. Our results are robust to the use of different datasets, using matching combined with difference-in-difference estimates, and alternative definitions of the treatment and control samples (described in detail in ‘Robustness analysis’ and in Supplementary Information F) .

To benchmark these changes in deforestation, we perform a cost-to-carbon analysis. The REDD+ project led to ~340,000 tCO 2 in avoided emissions per year, with an estimated cost per averted tCO 2 of $1.12. We further place this calculation into perspective in ‘Discussion’ (full details of the calculations can be found in Supplementary Information G) .

REDD+ impact on economic wellbeing and conservation attitudes

To measure how economic wellbeing and conservation attitudes in the REDD+ buffer zone communities were impacted by the project, we use detailed primary data from household surveys of N  = 841 households collected before (2014) and 5 yr after the programme started (2019). We find an overall increase of 0.222 s.d. in the economic wellbeing index over the 5 yr of the programme, which comprises a substantial and significant improvement (see Column 2 of Table 1 ). However, this increase cannot be attributed to REDD+ as there is no difference between REDD+ and non-REDD+ communities (the coefficient for the difference is small at 0.022 s.d.).

We also find no evidence that conservation attitudes changed due to the programme (see Table 1 , Column 3). Between the survey waves, the index for pro-conservation attitudes lowered substantially in both types of villages, by ~0.226 s.d. Although the attitudinal index is an outcome variable on its own right, it can also be viewed as a mechanism driving the impact of deforestation. We find no evidence of such a causal mechanism at work, so we explore other mechanisms in the next section.

For results on each survey indicator, as specified in our pre-analysis plan, refer to Supplementary Information E presenting standardized outcomes in Supplementary Tables 10 and 11 and unstandardized outcomes in Tables 12 and 13. We also include a series of secondary outcomes (consisting of alternative wealth measures) in Supplementary Tables 14 and 15 . Across all of these tables, the interaction term is never large or significant. Our results are also robust to using alternative quasi-experimental methods (described in detail in ‘Robustness analysis’ and in Supplementary Information F) .

It is important to emphasize that while the Gola REDD+ project has not improved local economic wellbeing or conservation attitudes, we can equally conclude that the project has not resulted in any economic harm or undermining of pro-conservation sentiments. Such ‘no harm’ is a vital feature for the viability of REDD+ projects that cannot be overestimated 19 . More importantly, our finding has enhanced significance in the associated literature, as it stems from a more robust methodological approach.

Potential mechanisms

We next explore other mechanisms that could explain the observed reduction in deforestation rates. Earlier studies have pointed to changes in the local labour allocation as a key factor affecting traditional small-scale agriculture in Sierra Leone 5 , 36 , 37 . Using the same household survey data collected before and after the intervention from both treated and control communities, we explore several such possible mechanisms in Table 2 .

We first assess an index of labour availability for the three main types of farms (upland, swampland and plantation). In REDD+ communities, there is a sharp reduction (0.545 s.d.) in access to farm labour (Column 1). In real terms, this reduction translates to a change in labour access of 0.534 on a scale from 0 to 3, where 3 indicates high labour access. This suggests that high labour demand for the activities supported through the REDD+ intervention reduces the amount of labour available for land clearing activities.

Secondly, incomes from farm wages are substantially higher (0.199 s.d.) in REDD+ communities since the start of the interventions (Column 2). This amounts to an increase of income from farm wages of 43.5%. Non-REDD+ communities at baseline had a yearly farm wage income of 29,000 Leones (or $6.7; in 2014, 1$ was ~4,320 Leones). The REDD+ project may have increased the opportunity cost of labour by providing alternative income possibilities. When farmers choose to pursue these alternative income possibilities, it leaves fewer labourers available for the local labour market (and thus reducing labour access). This in turn leaves fewer labourers for conventional, labour-intensive traditional agriculture, which is associated with deforestation. This lower labour availability and higher opportunity cost increase the local labour price, which increases income from working on other people’s farms. Because a decrease in labour access could also be driven by out-migration in the REDD+ communities, we test whether community population size changed due to the REDD+ programme and find no evidence for this (see Supplementary Table 16) . Also, note that this increase in farm wages does not result in overall increases in economic wellbeing.

Because the REDD+ programme aimed to provide more ecologically sustainable (or forest-friendly) alternative income sources, we examine trends in such sources. We explore income from the sale of non-timber forest products (NTFP). NTFPs are collected in forested areas (including within the GRNP). The activity is encouraged by the GRC, as it is non-invasive and creates incentives for protecting the national park. We find a substantial increase in NTFP incomes of 0.343 s.d. in REDD+ communities in the later period (Column 3). This translates to a 56.7% increase in NTFP income attributable to the REDD+ programme. In contrast, non-REDD+ communities exhibited no statistically significant change in their NTFP income and had a baseline value of ~108.400 Leones ($25).

Finally, we explore whether farmers switched to other crops, such as cocoa. In this context, cocoa is deemed more forest-friendly by the project developers, as cocoa is produced within forests. We find an increase in cocoa harvest size (0.196 s.d., Column 4) in REDD+ communities in the later period, although this is measured with substantial noise and the change is not statistically significant.

The market for voluntary carbon credits stemming from REDD+ projects is booming. Evidence on the deforestation and economic wellbeing impacts of these projects originating from independent, robust and causal studies is, however, surprisingly limited. We contribute to this significant knowledge gap by examining the impacts of an operational voluntary REDD+ project implemented in the buffer zone surrounding the GRNP in Sierra Leone. We find that the REDD+ programme decreased yearly deforestation rates by 30%. This shows that a relatively light-touch programme in the form of unconditional in-kind interventions can have beneficial effects on the natural environment. However, this impact is modest and the programme does not remove deforestation completely but rather helps slow down the deforestation trend. This raises questions about the sustainability of the programme, as it leaves no guarantees that the pressure on forests is removed, and communities have shifted to a new forest-friendly equilibrium. The results are in line with other recent studies that use different methods and focus on different types of carbon offset programmes 10 , 20 , 22 , 23 , 29 , 38 .

Despite the programme slowing rather than reversing deforestation trends, the avoided deforestation amounts to ~929 ha and ~340,000 tCO 2 in avoided emissions per year. Available cost–benefit analyses of similar land use projects suggest that this is at the higher end compared with what has been achieved elsewhere (for example, ref. 39 calculate a cost of $0.46 per avoided tCO 2 for a reforestation and afforestation project and ref. 23 report $0.84 per tCO 2 for a REDD pilot project) 23 , 39 . Cost comparisons across different REDD+ type projects can, however, be misleading as calculations, methods and assumptions vary considerably. Nevertheless, we can more safely conclude that this specific REDD+ project does appear to avert carbon at a cost that is considerably lower than the average sale price per offset within the evaluation period, at ~$3 per credit 8 . It is worth noting that land use projects are generally cost effective compared with other carbon reduction technologies, such as carbon capture and storage (CCS) which can cost between $40 and $400 per tCO 2 , or other expensive yet scalable technologies such as electric vehicle batteries ($350–$640), solar panels ($140–$2,100) and offshore wind turbines ($2–$260) 40 . While other low-cost interventions, such as behavioural nudges like OPOWER’s home energy reports 41 , exist to tackle carbon reductions, they are expected to produce relatively small emission reductions compared with land use programmes.

We also assess whether this type of REDD+ project can improve economic wellbeing. Using rare panel survey data, we find no clear evidence of changes to economic wellbeing, although we do observe increases in particular income streams (that is, NTFP collection). We also examine the mechanisms through which deforestation was reduced in the buffer zone. We find no evidence of improved conservation attitudes but hypothesize that the REDD+ project affected the opportunity cost of labour, which increased the local labour price via creation of alternative income possibilities. Some of these possibilities are sales of non-timber forest products and (forest-friendly) cocoa farming, although we find no evidence of other changes in income or production. As such, our findings contribute to the discussion about the potential impact of REDD+ projects on the economic welfare of local communities.

Taken together, our results highlight that interventions such as agricultural training and savings and loans programmes can slow the rate of deforestation while not causing economic harm to local communities. The interventions are not effective in generating positive changes to participants’ economic wellbeing. To achieve higher levels of environmental protection and improve the contribution of REDD+ to human wellbeing, more intensive interventions and considerable investment may be necessary. Our research also provides insights into the behavioural mechanisms that underlie the observed ecological impacts, suggesting that promoting targeted alternative sustainable livelihoods, such as non-timber forest products and cocoa, and addressing local labour market impacts are crucial to the success of REDD+ initiatives.

Rigorous independent evaluation methods for the impacts of voluntary REDD+ projects are increasingly called for by the scientific community. Stated carbon emission reductions by carbon verification agencies have recently been scrutinized 10 , 11 . With this study, we attempt to bridge this gap by using a rigorous identification strategy set up independently from the verification process. A clear-cut comparison between our findings and those from the verification is, unfortunately, not straightforward. Our evaluation focuses on the buffer zone (the area for which we have a credible counterfactual). In contrast, the certification agency Verra bases their assessment on avoided deforestation within the National Park as well as the buffer zone. Second, Verra focuses on a different time period for the verification (2012–2014), whereas we focus on the 5-yr period after GRC commenced REDD+ activities (2014–2018). With these caveats in mind, we can attempt an approximate per hectare comparison of our avoided deforestation results with those of Verra. Verra’s estimate of avoided deforestation is 940 ha yr −1 (averaged over the entire Gola REDD+ project area). This is close to our own estimate of 929 ha yr −1 (averaged over the buffer zone area alone) (see Supplementary Information H for details).

Previous impediments to undertaking rigorous evaluation studies of REDD+ projects related to the cost of data collection, uncertainty about which evaluation methods to use, and hesitancy from funders and project developers due to the fear that any disappointing short-term evaluations could jeopardize future financing 14 . Our experience from completing this study suggests that these concerns are increasingly waning. Improvements in technology and capacity building have reduced the costs of such evaluations, while there have been substantial advances in our understanding of the methods used. Further, we are now beyond the piloting phase of many REDD+ projects and thus have data over longer periods. We have also witnessed a shift in the mentality of project developers who are more willing to embrace best practice evaluation methods and receive support for setting up independent rigorous assessment methodologies (as, to their credit, did the agencies involved in the REDD+ project). Given the demand pressures to significantly increase the supply of these voluntary offsets, it is even more timely to call for more studies such as this one to add to the evidence base on voluntary REDD+ projects.

The analyses in this paper relied on three main sources of data: satellite data using a publicly available dataset 42 , border definitions (polygons) of all protected areas in Sierra Leone and survey data collected over three rounds in communities surrounding the GRNP (implemented in 2010, 2014 and 2019). Informed consent was obtained from all respondents interviewed. Ethical oversight was provided by Wageningen University and the University of Cambridge through their respective Institutional Review Boards. For our data analysis, we used R v.4.2.2 (2022-10-31), RStudio v.2022.07.2 and self-written code.

Deforestation data

The dataset in ref. 42 provides worldwide yearly data on forest loss. We used data from the 2001–2018 period. The dataset has a high resolution with a pixel size of 30 × 30 m (see Supplementary Information D for more information). This allowed us to use detailed information and recognize small-scale deforestation events (as is likely with traditional agricultural practices). Forest was defined as an area with >50% of vegetation taller than 5 m. Forest loss was defined as a change from a forested to a non-forested area. We disaggregated forest loss to the year and village (or community) level. To assign forest loss to specific villages, we used data from ref. 43 . First, simple Voronoi polygons were drawn for all villages in the seven Chiefdoms in which the GRNP lies (454 villages in total). Then, some of these polygons (228 in total) were adjusted in size on the basis of the estimated village population size obtained through a village survey in 2010. Unsurveyed villages were not weighted (our results are similar when we run the analysis for weighted polygons only; see Supplementary Table 21) . The resulting predicted village polygons were verified using GPS boundary data collected for a sample of 98 of the villages (see Supplementary Fig. 3 for the polygon map and Supplementary Information D for more information on the estimation method). The data on locations and population sizes were based on a survey of 228 villages in 2010. Finally, we counted the number of pixels indicating forest loss in a village polygon in a given year and calculated the percentage forest lost by dividing the area deforested by the size of the polygon. By using the percentage, we could compare villages with different-sized land holdings.

Protected Areas definition

We placed the observed deforestation rates in the GRNP into context by examining other PAs in Sierra Leone. This analysis was based on a map provided by the Sierra Leonean Ministry of Agriculture, which we used to infer the exact borders of the PAs. We examined all existing national parks, forest reserves and game sanctuaries with a legal protection status. The deforestation data in ref. 42 were used to examine forest loss over the 2001–2018 period for each PA separately. We also examined forest loss in buffer zones, which provide important corridors for endangered species and prevent encroachment. We used a 4-km distance from the border to define this buffer zone, to be consistent with the GRNP’s buffer zone. We only considered buffer areas that fell within the national borders of Sierra Leone. Deforestation results for these national parks are shown in Supplementary Information B . We used data from the World Database on Protected Areas to extend our analysis to PAs in Guinea and Liberia.

Survey data

We used primary survey data collected in Sierra Leone during three survey waves. During March/April 2010, Wageningen and Cambridge University researchers collaborated with GRC to implement a baseline survey in villages in the seven chiefdoms surrounding the GRNP. GRC selected 200 villages that were closest to the National Park and most likely to have community forests with high biodiversity value. From this list, 11 did not exist (anymore) and the survey was subsequently implemented in 189 communities. This survey was also the source of village locations and sizes, which were used in the Voronoi polygon definition in ref. 43 . In each village, 15 households were randomly sampled and interviewed regarding demographics, economic outcomes, hunting and gathering behaviour, and attitudes towards conservation. We implemented a second survey in April 2014, just before the start of REDD+ activities. From the villages included in the 2010 survey wave, we randomly selected 30 REDD+ villages, that is, those eligible for REDD+ benefits. These communities all lie within a 4-km buffer zone around the National Park. We also selected 30 non-REDD+ villages which were randomly selected from villages 4–25 km from the National Park boundary. The sampling was stratified by regional quadrants to ensure representation of villages between the GRNP boundary and the border with Liberia. One of the REDD+ villages was removed from the sample as it no longer existed, bringing our full sample down to 59 (see Supplementary Fig. 5 for a detailed map). The same households as in 2010 were interviewed. During this survey wave, 841 households in total were surveyed across the 59 villages, with an average of 14 households per village (some villages had fewer than 15 households). For the follow-up survey during April 2019, we revisited each household included in the 2014 survey. If the head of the household was not available, we selected a representative of the household. We recontacted 81% of the 2014 sample in 2019. The 2014 and 2019 survey waves were used for the main analysis, whereas the 2010 round was used to explore parallel trends (see ‘Parallel trends’). An attrition analysis for all primary outcomes shows no bias in attrition between REDD+ and non-REDD+ villages (see Supplementary Table 8) .

Survey outcomes

We assessed two main survey outcomes: an index of outcomes related to economic wellbeing and an index related to conservation attitudes (a description of family outcomes and the variables they consist of can be found in Supplementary Table 3 and descriptive statistics in Supplementary Tables 5 and 6) . By grouping our variables into families, we reduced the number of statistical tests necessary. We used the approach in ref. 44 to combine variables with different units into families. This worked by first normalizing all variables and then taking the raw mean of these z -scores. If an observation was missing for a certain variable, this was imputed at the own-group mean (that is, by survey round and treatment status).

The economic wellbeing family consisted of data on income, expenditures, resilience, productive loans and assets. Income is the sum of a very broad range of income categories which includes almost all sources of income, thereby increasing our precision. We asked this question over the previous year. We also looked at two forms of expenditures as a more robust estimate of income. We asked about expenditures in the previous month on a set of common consumption items. We also asked about yearly expenditures on larger, less regular items. Resilience was measured as a dummy indicating whether individuals were able to cope with an emergency in the previous year. Durable loans were the sum of loans in the previous year for productive/durable activities. Assets was the sum of a common set of assets owned, such as tables, beds and housing materials. Outcomes that are expressed in monetary terms (income, expenditures and productive loans) were transformed using the inverse hyperbolic sine function, reducing the variance of the outcome.

The conservation attitude family consisted of stated attitudes, knowledge of conservation rules, use of sustainable farming practices and perception of human–wildlife conflict. Stated attitudes were responses on a 5-point Likert scale to four questions related to the GRNP and conservation in general. Knowledge of conservation rules was assessed by asking five questions about what is allowed and not allowed in the national park (on mining, gathering, fishing, logging and hunting). Sustainable farming was the number of sustainable farming practices used, for example, on land use. Finally, we asked how big of a problem human–wildlife conflict is (on a 0–3 scale). Increased human–wildlife conflict is often associated with the creation of the national park, which might have increased animal populations.

We also explored other potential mechanisms, mainly related to changes in the local labour market. Labour is one of the main seasonal constraints for agricultural production in Sierra Leone, with over 65% of households reporting labour shortages in the agricultural season in a nationwide survey 36 . To assess labour shortages, we asked respondents how much of a problem it is to get labour (scale 0–3) for the three main types of farms and calculated the average value. We also assessed income from farm wages in the previous year and looked at yearly income from NTFPs. NTFPs are an important alternative form of income associated with the creation of the national park, as these are explicitly allowed to be collected and will be more plentiful if the park is well preserved. Finally, we included estimates of cocoa harvests in the previous year.

Empirical strategy

To estimate the effect of REDD+, we used a standard difference-in-difference (or BACI) model:

where Y i j t refers to our set of outcomes (either deforestation rates at the community level or a household-level family indicator), REDD j is a dummy for villages within the buffer zone (that is, REDD+ eligible communities) and post t is a dummy referring to the second survey wave (2019). β 3 is our coefficient of interest. i indexes the household level (only for household-level outcomes), j indexes the village level and t the survey wave. For our household-level outcomes, we clustered standard errors at the village level ( ε i j t ). Only households for whom we had panel data (for example, they were interviewed in both rounds) were included. This estimator provides us with an unbiased estimate of the treatment effect if we can assume that without the project, the communities would have trended similarly (parallel trends assumption). We explored this assumption in the next section.

Parallel trends

Our main identifying assumption is that outcomes in REDD+ communities would have trended similarly as those in non-REDD+ communities had the REDD+ project not been implemented. Even though this parallel-trends assumption is fundamentally untestable, we did several comparisons of REDD+ and non-REDD+ communities before the REDD+ intervention to show that this assumption is likely to hold.

Generally, REDD+ and non-REDD+ communities exhibited remarkably few significant differences in village-level as well as household-level indicators, of which many (such as the amount of human settlement, geographical factors, distance to markets, agricultural production) are important drivers of deforestation (see Supplementary Table 4) . At the village-level, only village size was significantly smaller in REDD+ communities compared with non-REDD+ communities. At the household-level, slightly fewer household heads obtained some form of secondary education in REDD+ communities. Through a matching procedure described in ‘Robustness analysis’, we addressed these differences and find similar results.

For our deforestation outcome, we had multiple rounds of pre-REDD+ data available, that is, the years 2001–2013 in Fig. 3 . Trends (and levels) were very similar before REDD+ activities, which suggests that this would have continued without REDD+. There was a break in levels coinciding with the inclusion of satellite data from Landsat 8, resulting in more accurate measures of deforestation. However, this change in levels affected both types of village (robustness of this data is further addressed in the next section). The presence of parallel trends before the start of REDD+ is also visible when we ran an event-study model in which we estimated year-specific effects of REDD+ (shown in Supplementary Fig. 4 ). All year-specific treatment effects before 2014 were non-significant and close to zero, except for the year 2002 when differences were still very small.

For our survey outcomes, we made use of the unique opportunity of having access to two rounds of pre-REDD+ data (the 2010 and 2014 rounds of data). We first inspected whether there were any meaningful level differences between REDD+ and non-REDD+ villages (see Supplementary Tables 5 and 6) . Differences were small and, in most cases, non-significant. In 2010, yearly irregular expenditures and income from NTFPs were slightly lower in REDD+ villages. This difference dissipated by 2014, hence we doubted that access to cash (important to mobilize labour for deforestation) affected our results. Sustainable farming practices were higher in 2010 but the difference also faded by 2014. In 2014, awareness of conservation norms was somewhat higher in REDD+ communities. If this means that there is not as much scope for this awareness to increase because of REDD+, this will bias our results downward. In 2014, human–wildlife conflict was also higher in REDD+ communities. This could be caused by proximity to the National Park, which contains much of this wildlife. One way farmers could deal with this is by removing trees surrounding their farms, thereby reducing wildlife access. If this is the case, the reduction in deforestation we found is a lower bound of the real effect.

Note that these level differences are only problematic if they affect the trend of our outcomes, as level differences drop out in the difference-in-difference approach. To assess systematic differences across both data waves, we re-ran our difference-in-difference model for the 2010–2014 data on the main outcomes and mechanisms for which data were available (Supplementary Table 7) . In no case was the 2014*REDD+ coefficient significant: we found no significantly different trends between the two groups.

As a robustness check, we combined our difference-in-difference model with matching techniques and found similar results on our deforestation, economic wellbeing and attitudes outcomes (see Supplementary Tables 19 and 23 , and ‘Robustness analysis’ below).

A potential concern is that our result is driven by leakage from the REDD+ area to the control area. This seems unlikely as REDD+ communities have few incentives to move their activities elsewhere. Cutting trees is legal in the buffer zone of the park (that is, the REDD+ communities). Second, the REDD+ project is not conditional on deforestation outcomes in the buffer zone. Third, cutting trees is labour intensive and typically done on foot. Thus, it is not very likely that REDD+ communities moved to non-REDD+ areas for farming or other activities that require cutting trees. An additional source of leakage could be that treated communities bought more wood on the market sourced from the control areas. This is unlikely because wood demand in the area mostly stems from urban construction. Any market leakage is therefore expected to be negligible.

Robustness analysis

We ran multiple robustness checks to address potential concerns about the quality of the satellite data and the comparability of REDD+ and non-REDD+ villages (see Supplementary Information F for all robustness analyses). First, we used an alternative deforestation dataset, the Tropical Moist Forest data in ref. 45 . We found a similar negative effect of REDD+ on forest loss (the effect size is −0.759 percentage points, which amounts to ~27% less forest loss yearly; see Supplementary Fig. 6 and Table 17) .

To improve comparability between REDD+ and non-REDD+ villages, we used matching (both propensity score matching and coarsened exact matching) in combination with difference-and-difference for both deforestation datasets. We found consistently negative effects of REDD+ on deforestation, with coefficients varying from −0.618 vs −1.297 percentage points forest loss per year (Supplementary Table 19) .

A potential concern is that the higher-precision Landsat 8 imagery from 2013 is driving our deforestation result. Forest loss measurements could, for instance, be less precise as proximity to the national park increases. We ran multiple tests to address this concern (Supplementary Table 20) . First, we ran the same model but restricted our sample to all communities within 8 km of the park (thereby defining only communities in the 4–8 km zone as the control group), which hardly changed our estimates. We ran a falsification check by removing the REDD+ communities from our sample and defining the 4–8 km zone as the treatment group, comparing it to the remaining non-REDD+ communities. The deforestation result disappeared, indicating that proximity to the national park is not likely to affect forest loss measurement. We also ran our model with Landsat 8 data only, for the years 2013–2018, excluding the lower-precision years, which also hardly changed our estimates (Supplementary Table 21) . Last, we ran our model excluding all villages that lie within 1 mile of the GRNP. GRC had been active in this area before the start of the REDD+ programme (details about their activities can be found in Supplementary Information C) . We found that the effect is significant, albeit somewhat smaller (−0.741 vs −1.032 percentage points; see Supplementary Table 20) . This could suggest that previous conservation activities had a long-term effect on deforestation in this area. Alternatively, it could mean that REDD+ is more effective in villages near the national park.

In sum, these robustness checks suggest that the REDD+ programme reduced deforestation rates by ~1 percentage points or 30% yearly.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The data used in this paper are available in a GitHub repository (repo: MandyMalan/gola-redd-impact). Raw survey data and household-level covariates dataset used for matching in our Robustness section are not published to ensure anonymity of our respondents. Data can be made available upon request. Public data used in this paper are from ref. 42 (v.1.6 available at: https://earthenginepartners.appspot.com/science-2013-global-forest/download_v1.6.html ), ref. 45 (available at https://forobs.jrc.ec.europa.eu/TMF/data.php#downloads ) and Worldwide Database on Protected Areas (available at https://www.protectedplanet.net/en/thematic-areas/wdpa?tab=WDPA ).

Code availability

All code needed to reproduce this paper are published in a GitHub repository (repo: MandyMalan/gola-redd-impact). Code used for cleaning data is not published and can be made available upon request.

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Acknowledgements

We thank the Royal Society for the Protection of Birds (RSPB) and the Gola Rainforest Conservation LG (not for profit company), BirdLife International, E. Mokuwa, P. Richards and M. Ross for their collaboration in this project. We thank GCRF QR at the University of Cambridge and NWO for financial support (grant VI.Vidi.191.154 received by M.V.). We acknowledge the loyalty and hard work of the team of field enumerators and the patience and cooperation of interviewees.

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Mandy Malan

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Grantham Research Institute of Climate Change and the Environment, London School of Economics, London, UK

Elisabeth Gsottbauer

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Paul Hofman

Department of Land Economy, University of Cambridge, Cambridge, UK

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Contributions

All authors conceptualized the project; M.M. and P.H. curated the data; M.M., P.H. and T.S. undertook formal analyses; M.V. and A.K. acquired funding; M.M., P.H., M.V., T.S. and E.G. performed the investigations; all authors designed the methodology; M.V. administered the project; M.M., A.K., P.H. and M.V. supervised the work; all authors validated the findings; M.M. and P.H. visualized the data; M.M., P.H. and M.V. wrote the first draft, and all authors contributed to the review and editing of the paper.

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Correspondence to Andreas Kontoleon .

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The authors declare no competing interests. The authors did not receive financial or non-financial benefits from the donors, or any other partners related to any of the interventions presented here. The Cambridge Centre for Carbon Credits (4C) has no commercial interest in carbon credits. T.S. worked as a conservation scientist for the Royal Society for the Protection of Birds (RSPB) until 2020. The RSPB maintains close ties with the Gola Rainforest REDD+ programme. T.S. had no direct involvement in the design and implementation of the REDD+ programme. We pre-registered this study with the EGAP-OSF registry after data collection, but before data processing and analysis (OSF ID: 8n7h6).

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Malan, M., Carmenta, R., Gsottbauer, E. et al. Evaluating the impacts of a large-scale voluntary REDD+ project in Sierra Leone. Nat Sustain 7 , 120–129 (2024). https://doi.org/10.1038/s41893-023-01256-9

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Assessing the Implications of Deforestation and Climate Change on Rural Livelihood in Ghana: a Multidimensional Analysis and Solution-Based Approach

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• Published: 27 September 2024

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• Richard Kwame Adom 1 ,
• Memory Reid 2 ,
• Gbenga Abayomi Afuye 3 , 4 &
• Mulala Danny Simatele 1  

The Ashanti region in Ghana, abundant in natural resources such as forests and vegetation biomes, significantly supports the livelihoods of a significant portion of the population. The sustainable management of forest resources remains a significant challenge to achieving environmental and economic growth and poverty alleviation. The study aims to identify the drivers of deforestation and assess its impact on the livelihoods of the poor and vulnerable communities in the Ashanti region. The study utilized qualitative and space-based data to examine the patterns of vegetation cover and deforestation from 2000 to 2020. The results revealed moderate to sparse vegetation in Ashanti from 2002, 2005, 2011, 2015, 2017, and 2018, with no vegetation in the northcentral part, attributed to climate change, agricultural practices, government policies, and deforestation-related disasters. The study found a significant correlation ( R ² = 0.8197) between years and deforestation areas, especially in 2018 at around 16,000 Sqkm, indicating an exponential increase with severe implications for sustainable livelihoods. Much of these changes were reflected in 2020 with a high peak of deforestation towards the southeastern parts of the region. Additionally, the results show that the poor groups are not passive actors but are actively involved in identifying systems and processes through which to build their adaptive capacity and resilience to environmental and climate change-induced changes. The findings provide evidence-based and all-inclusive approaches that would encourage vulnerable and marginalized groups to participate in the co-production and co-creation of policies and strategies. This outcome is geared towards transformative and sustainable communities while ensuring efficient and effective response and recovery capacities of deforested lands.

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Introduction

One of the greatest environmental challenges facing the world today is deforestation (Bologna, Aquiro ( 2020 )). Woodlands and forests play a crucial role in Earth’s climate resilience, acting as carbon sinks, slowing global warming, contributing to the hydrological cycle, and providing atmospheric moisture through transpiration (Lawrence et al. 2022 ). Similarly, forests act as natural water filters and regulators, maintaining freshwater resources, reducing soil erosion, sifting pollutants, and regulating water flow (Lawrence et al. 2022 ; Shah et al. 2022 ). Forests and forest products provide livelihoods for over a billion people globally, including timber, non-timber products, and fuelwood, particularly in developing nations (Zhu et al. 2023 ). Forests offer crucial ecosystem services, supporting agricultural production, water generation, soil fertility, and non-timber forest product provision. Forests and agriculture are crucial for the economic development and livelihood support of people in various countries. Accordingly, forest products and activities generate $250 billion annually globally, supporting numerous local, national, and international economies through tourism, trade, and manufacturing (Arce 2019 ). Therefore, forests significantly contribute to global income generation and household food security (Amoah and Korle 2020 ).

Despite the numerous benefits associated with forests and woodlands, the rate at which forests are disappearing across the globe is alarming (Ritchie and Roser 2024 ). Many scholars describe deforestation as the process of clearing forests for various purposes such as agriculture, settlement, infrastructure development, and mining (Lund 2018 ; de Oca et al. 2021 ; Brandt et al. 2022 ). Boafo ( 2013 ) also defined deforestation as the conversion of forested land for other purposes or a permanent reduction in canopy cover. Global deforestation, primarily in developing nations, causes 13 million hectares of land loss annually, accounting for 31% of the world’s total forest cover, with countries like Brazil, Indonesia, Congo, Cameroon, Australia, the US, and Bolivia contributing over 60% (Boafo 2013 ; Brandt et al. 2022 ; Lawrence et al. 2022 ). Ritchie ( 2021 ) reported a 4.7 million hectare annual net loss of global forest since 2010, a figure comparable to the combined size of the United States and China. Many researchers and environmental advocators attributed deforestation to various factors, including farming practices, land use, development, and natural factors like weather extremes, according to researchers and environmental advocates (Prevedello et al. 2019 ; Pickering, Guglyuvatyy ( 2019 ); Cadman et al. 2019 ). Igini ( 2022 ) identified poor farming techniques, mining, infrastructure development, and urban expansion as the primary contributors to global deforestation. Global deforestation causes biodiversity loss, climate uncertainty, water cycle disruption, soil erosion, Indigenous community displacement, land rights disputes, and health impacts due to cultural and physical displacement (Butler 2019 ; Eiras-Barca et al. ( 2020 )). Deforestation leads to short-term economic benefits but long-term costs, including reduced agricultural outputs, increased unemployment, and decreased revenue in ecotourism, trade, and industries (Arce 2019 ).

Ghana covers 35% of the total land area, with 7.9 million hectares of forested land (Kyere-Boateng and Marek 2021 ). In 2022, Ghana experienced a 70% increase in the loss of 18,000 hectares of its primary forest, marking the largest loss in recent years (Afele et al. 2021 ). Boafo ( 2013 ) attributed 95% of deforestation in Ghana to demographic and economic pressures, policy, institutional lapses, increased land demand, fuel wood, illegal logging, mining, infrastructure development, and community expansion. The Food and Agriculture Organization of the United Nations (FAO) reports that 96.25% of 7.6 million hectares are primary or naturally regenerated forests, while 3.75% of 297,000 hectares are planted forests (FAO 2020 ; Afele et al. 2021 ). Boafo ( 2013 ) and Kyere-Boateng et al. ( 2021 ) identified cash crop cultivation, illegal lumbering, mining operations, and settlement expansion as primary causes of deforestation in Nigeria’s Southern regions, alongside unsustainable charcoal production and forest fires. Acheampong et al. ( 2019 ) and Sasu ( 2022 ) report that over 60% of Ghana’s primary forests have been lost, with an annual deforestation rate of 3.51%, resulting in over 315,000 hectares of loss and projected to surpass 4.7 million by 2030. Natural factors like hurricanes, fires, parasites, and floods contribute to deforestation across the country (Amoah and Korle 2020 ).

Deforestation in the country has severe environmental and human well-being impacts, affecting sustainable livelihoods, disrupting environmental functions, and destroying forest ecosystems (Afele et al. 2022 ). Deforestation in Ghana poses significant socioeconomic risks due to climate unpredictability, soil degradation, species extinction, and rapid decline in the river and stream water levels (Khodadadi et al. 2021 ). Kyere-Boateng et al. ( 2021 ) posits that between 1990 and 2021, over 200 deforestation-related disasters occurred, resulting in over 100,000 deaths and $2 billion in economic losses, equivalent to 0.5% of Ghana’s GDP. Chirwa and Adeyemi (2020) found that the agriculture and forestry sectors employ over 60% of the population, including 53% of women, resulting in an indirect cost of over US$400 million, equivalent to 0.7% of 2020 GDP. Previous studies have explored the social and economic impacts of deforestation on livelihoods, the need for policy reforms, socioeconomic issues, governance, and advancements in deforestation rate analysis, among other topics (Boafo, 2013 ; Khodadadi et al. 2021 ; Adom et al. 2023 ). Amoah and Korle ( 2020 ) and Acheampong et al. ( 2019 ) conducted studies on deforestation in Ghana, using remote sensing techniques, and suggested modifications to farming techniques to increase yield. Numerous studies have extensively explored socioeconomic factors linked to deforestation, governance, and advancements in deforestation rate analysis techniques (Boafo 2013 ; Acheampong et al. 2019 ; Amoah and Korle 2020 ; Afele et al. 2022 ). Nevertheless, there is a lack of literature on community-based forest management strategies and adaptations for the long-term social and economic impacts of deforestation on Ghana’s economic development. Hence, this study explores the impact of deforestation and climate change on the socioeconomic livelihoods of the poor and rural population in Ghana, filling a gap in the existing literature.

Theoretical Perspectives on Drivers of Deforestation and its Impacts on Climate Change in Ghana

Figure 1 provides a detailed explanation of the root and proximate causes of deforestation, specifically in Ghana. The factors driving the proximate causes of forest degradation can be categorized into six areas: demographic factors, economic factors, technological factors, policy and institutional factors, and cultural factors. Demographic factors include natural increases in fertility and mortality rates, migration patterns, population density, distribution, and life cycle features. Economic factors include market growth, commercialization, urbanization, and special variables like price increases and comparative cost advantages. Technological factors include agro-technical change, technological advancements in the wood sector, and changes in agricultural inputs and methods. Policy and institutional factors include formal policies, policy climate, property rights, public attitudes, values, and beliefs, and individual and household behavior driven by concern or rent-seeking behaviors. Tacconi et al. ( 2019 ) highlight illegal practices in the forest sector, including government officers authorizing contracts, illegal harvesting, under-declaring forest product volumes, underpricing of timber, and land invasion. The increase in illegal forest practices has been detrimental to the conservation of forests. Therefore, Understanding these causes can aid in developing comprehensive strategies to combat deforestation.

The underlying causes of deforestation in Ghana. Source: Caroline Sartorato Silva França

Illegal forest practices, such as government-authorized contracts, illegal harvesting license sales, under-declaration of forest product volumes, under-pricing of timber, and invasion of protected land, significantly impact forest conservation (Tacconi et al. 2019 ). Moreover, deforestation in Ghana is primarily driven by poverty, economic factors like logging, expanding land for food production, fuel wood extraction, and both legal and illegal mining operations (Gyamfi et al. 2021 ). Figure 1 shows the complex interplay of immediate activities and deep-rooted systemic factors that contribute to deforestation in Ghana. Accordingly, Ghana’s licensed timber production companies are legally authorized to harvest timber in compliance with government regulations (Astana et al. 2020 ). The Forestry Commission of Ghana mandates that these companies must adhere to sustainable practices and guidelines (Frey et al. 2021 ). The rise in production activities of these companies has led to an increase in the Annual Allowable Cut (AAC) (Astana et al. 2020 ; Frey et al. 2021 ). However, the maximum volume of timber legally harvested within a year is monitored to ensure sustainable forest management. Legally licensed companies have increased production in recent years to meet domestic and international timber demand and control illegal logging. The balance between increased AAC and forest resources must be managed effectively to prevent overexploitation and degradation. On the other hand, non-compliance with Ghana’s regulations could lead to severe environmental and economic consequences, including overexploitation of forest resources, deforestation, biodiversity loss, ecosystem disruption, reduced carbon sequestration, and worsened climate change. Sustainable practices are crucial for maintaining ecological balance, economic stability, and social well-being, but communities reliant on forests may face threats.

Scholars like Afele et al. ( 2021 ), Fagariba et al. ( 2018 ), and Teye ( 2008 ), argued that deforestation in Ghana is influenced by five main factors rather than one single variable. Human activities at the local or community level, such as agricultural expansion, illegal mining, logging, and forest products, are primarily driven by increasing food production and population growth (Peprah et al. 2022 ). Deforestation in Ghana is influenced by social, demographic, economic, technological, policy institutional, and cultural factors, creating a complex system that drives deforestation activities, as postulated by Nyako et al. ( 2023 ) and Asamoah et al. ( 2023 ). Fagariba et al. ( 2018 ) argue that rural poverty and population growth lead to the expansion of agriculture and illicit mining, often exacerbated by weak land tenure policies, inadequate enforcement, and cultural practices that shape individual and community behavior towards forests. Figure 1 summarizes the key factors that drive deforestation in Ghana.

The Implication of Deforestation on the Environment and Sustainable Livelihoods in Ghana

Deforestation leads to significant environmental impacts, including biodiversity loss, climate change, water cycle disruption, global health issues, desertification, economic impact, soil erosion, and degradation, extending beyond national borders (Butler, 2019 ; Adolph et al. 2023 ). Consequently, the implications and consequences of these actions extend beyond national boundaries (Butler, 2019 ). Deforestation in Ghana significantly impacts the environment and social and economic livelihoods and contributes to the Earth’s climate system by acting as a carbon sink (Amoah et al. 2019 ; Brack 2019 ). Deforestation in Ghana has led to increased temperatures and changes in rainfall distribution, creating unpredictability in climatic conditions, as it interacts with global warming caused by greenhouse gas emissions (Cudjoe et al. 2021 ; Li et al. 2022 ). Figure 2 summarizes the environmental impacts of deforestation experienced in Ghana.

Environmental implications of deforestation in Ghana. Source: Patil Amruta collection on deforestation

Deforestation in Ghana has significantly reduced biodiversity over the past five decades, particularly in tropical rainforests, which are crucial habitats for millions of organisms, causing significant loss (Butler 2019 ; Jackson et al. 2020 ; Shivanna 2022 ). Over 60% of Ghana is covered by forests, housing 80% of all animal and plant species. Deforestation destroys these forests, affecting the genetic diversity of the species that inhabit them (Butler 2019 ; Baidoo et al. 2023 ). Deforestation and genetic diversity decline, impacting ecosystem health and species diversity, while trees regulate water flow, prevent soil erosion, and preserve water resources (Wiekenkamp et al. 2019 ; Muluneh, 2021). Deforestation has severely impacted the country’s land cover, exposing it to extensive wind and rain, thereby increasing soil vulnerability and proneness to erosion (Butler 2019 ). Deforestation significantly impacts social, economic, and political sustenance, especially for Indigenous and rural communities in Ghana, who rely on forests for livelihoods such as food, medicines, fuelwood, and jobs (Wale et al. 2022 ). Deforestation leads to displacement and migration of communities reliant on forests, causing cultural loss, loss of community cohesion, and reduced access to resources and land (Pearson et al. 2021 ). Furthermore, the loss of forests has resulted in a decrease in the availability of essential forest resources like timber and non-timber forest products, which are crucial for community livelihoods. Similarly, Fagariba et al. ( 2018 ) discovered that deforestation impacts livelihood activities in agriculture, mining, wood logging, and hunting, affecting employment, food security, income, economic growth, family cohesion, migrations, and cultural identity. Unsustainable forest management leads to negative impacts on livelihood activities (Pearson et al. 2021 ). Figure 3 summarizes the implications of climate change on sustainable livelihoods.

Implications of deforestation on livelihoods in Ghana. Source: Field survey 2023

Many of the above-mentioned implications are exacerbated by poor policies and weak regulations and monitoring systems (Asamoah et al. 2023 ). To enhance sustainable livelihoods and reduce deforestation, it is crucial to implement intensive education, awareness creation, and effective forest management strategies (Lopez-Carr 2021 ).

Materials and Methods

In this section, research design, sampling and sample population, data collection and techniques, and data analysis were explored.

The study area is the Ashanti region in the Southern part of Ghana. Ashanti is located between 6°44’49.357”N and −1°31’15.105’E spanning 24,389 km 2 (9417 sq mi) in Ghana (Fig. 4 ), with an approximately total population of 4,780,380 inhabit-ants (Khan-Andersen 2024 ). The region was chosen as a case study due to its geographical location, deforestation trends over the past three decades, and simultaneous socioeconomic conditions across the country. The climatic condition in the region is sub-tropical, with an average rainfall of between 1100–1800 mm, a temperature range of 21° to 32 °C, and higher humid conditions (Amponsah et al. ( 2002 )). The Ashanti region, a hub for commercial crops like cocoa and palm trees, is characterized by a mix of cropland and tree cover, with the Atewa Range Forest Reserve and Tano-Offin Forest Reserve being the most dominant (Appiah and Guodaar 2022 ). The region’s social and economic dynamics, along with population growth, energy costs, poverty, and unemployment, significantly impact the use of natural resources, particularly forests, leading to exponential deforestation (Kyere-Boateng et al. 2021 ).

Map of Ashanti region in the southern part of Ghana

Research Design

The study utilized a mixed-method approach, combining quantitative and qualitative methods, to gather data on the extent of deforestation in Ghana. In addition, the study conducted a comprehensive literature review on the impact of deforestation on climate change and the social and economic livelihoods of the population. The study utilized geospatial data analysis to examine the rates and trends of deforestation in the region between 2000 and 2020, using a quantitative technique (Afuye et al. 2022 ; Mpanyaro et al. 2024 ). The approach involves exploring the relationship between demographic patterns and the rate of deforestation in the study area. The study utilized a qualitative approach through interviews to evaluate the effects of deforestation on the social and economic livelihoods of the population and identify the causes and drivers of deforestation in the region (Enbakom et al. 2017 ; Ken et al. 2020 ). Additionally, the study extensively reviewed the existing policies and regulations in the region and country to evaluate their effectiveness and applicability in addressing deforestation issues.

Remote sensing data acquisition

Remote sensing data from Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) MOD13Q1 and 16-Day L3 Global 250 m imagery between 2000 and 2020. The Modis data was downloaded from the United States Geological Survey (USGS) Earth Explorer ( https://earthexplorer.usgs.gov ) for the analysis. The Ashanti boundary shapefile, sourced from Esri, was utilized to determine the scope of analysis on the area of interest (Zhu and Woodcock 2012 ; Wang et al. 2020 ). The satellite-derived vegetation index such as the NDVI for the respective years under review was clipped using the Ashanti boundary shapefile to narrow down the analysis to the coverage of the study. This was done with the clip option in the geoprocessing tool of ArcGIS. The MODIS NDVI data values range from −1999 to 10,000, and −2000 is the fill value. Value “−1999” is assigned to any VI computation between “−1998” and “−10,000”. To normalize the values, a scale factor of 0.0001 is applied to the pixels by multiplication (Zhu and Woodcock 2012 ). The NDVI resulting values were used for further analysis. For the study, the NDVI imageries were classified into four classes with values ranging from –0.1 to 0 grouped having no vegetation, 0–0.2 as sparse vegetation, 0.2–0.5 as moderate vegetation, and 0.6–1 as dense vegetation being the class containing the trees and forest. The different classes of the imageries were reclassified to unify the range of pixel values within each class (Zhu and Woodcock 2012 ), for further processing. The no vegetation class, sparse vegetation class, moderate vegetation, and dense vegetation were assigned the values −1, 1, 2, and 3 respectively.

Sampling techniques and study population

The study utilized probability in simple random sampling and non-probability in purposive sampling technique for data collection. The complexities of deforestation activities in Ghana and their impact on social and economic livelihoods necessitated the gathering of diverse perspectives and viewpoints. According to Campbell et al. ( 2020 ), purposive sampling is the intentional selection of informants based on their special knowledge and expertise and the willingness and ability to elucidate a specific theme, concept, or phenomenon. This sampling technique was used to select a total of 15 participants for interviews for the qualitative aspect of the study. Based on research questions, the researchers applied judgemental criteria to select and engage in online interviews with a total of seven respondents. The group included employees from the Ministry of Land and Natural Resources, five from the Forestry Commission of Ghana, and three Ashanti region community leaders. This study utilized simple random sampling to collect quantitative data through a survey, ensuring equal probability of each sample being selected (Marradi, 2022 ). The author asserts that the simple random approach aims to prevent any potential bias in the representation of the entire population. This technique was considered because it assisted the researchers in sampling a large pool of respondents for the survey. A total of 1000 respondents were selected from five communities, namely, Konongo, Bekwai, Obuasi, Agogo, and Abetifi, all in the Ashanti region. To achieve the proportional representation of the population based on socioeconomics, the Probability Proportional to Size sampling is computed in Eq. ( 1 ).

where, s was employed to compute the sampling population in each community. In this formula, P is the proportion of the community in the sampled population, C is the community population, T is the total population, and S is the sample size. Using this Eq. ( 1 ) for the analysis, the latest statistical information was obtained from StatsGhana (Ghana Statistical Service GSS ( 2021 )). The communities identified include Konongo  ≈  41,238; Bekwai  ≈  7267; Obuasi  ≈  175 043; Agogo  ≈  28,271; and Mampong  ≈  79,726. The computation of the: Konongo (41,238/331,545   ×   1000  =  124); Bekwai (7267/331,545   ×   1000  =  22); Obuasi (175,043/331,545   ×   1000  =  528); Agogo (28,271/331,545   ×   1000  =  86) and Mampong (79,726/331,545   ×   1000  =  240) . Nonetheless, the study used a systematic sampling method, selecting respondents aged 18 or older who had resided in the community for at least two years. The questionnaires were self-administered with the help of two field assistants who were conversant with the area.

Data Analysis

The study utilized remote sensing and GIS to analyze forest cover change in the Ashanti region, assessing the quality of the green vegetation condition index from 2001–2020. The Normalized Difference Vegetation Index was utilized to measure vegetation cover change and deforestation events in Ashanti, enabling a comprehensive mapping of vegetation cover status over time.

for the years 2000 to 2020. The NDVI themes used in the image classification method represent the most consistent temporal gaps within the region, as depicted in Fig. 5 . The selection of pixels was made to capture a clear and significant cover change trajectory, considering the response time of landscape transformation to land cover changes (Shawul and Chakma 2019 ; Aspinall et al. 2021 ). The data was collected and analyzed between 2000 and 2020 using the intersect geoprocessing method for change detection (Bashir and Ahmad 2017 ). The method was employed to assess the pixel level at various changes in the landscape over time (Rajkumar 2022 ). The results were utilized for a change detection analysis of historical forest cover and the rate of vegetation state transitions (Fig. 5 ).

Workflow of the image classification methods

In addition, the study utilized descriptive statistics of SPSS Windows Version 21 to analyze questionnaire data, focusing on demographic characteristics, the socioeconomic status of households, and the impact of deforestation on livelihoods (Kiger, Varpio ( 2020 )). The research team utilized SPSS software for data analysis, creating visual aids like frequency Tables and graphs and thematic analysis method for qualitative data from interviews and literature review. Thematic analysis is a method used to analyze qualitative data by identifying key messages or themes from interviewees, aiding researchers in sorting and grouping data for interpretation and discussion (Kiger, Varpio ( 2020 )). The study utilized a Pearson correlation coefficient to establish strong connections between deforestation and sustainable livelihoods among the population in the region and country (Lahai et al. 2022 ; Nyirarwasa et al. 2024 ). The Pearson correlation coefficient is computed in Eq. ( 2 ).

where, \({X}_{i}\) represent the amount of forest area lost due to deforestation over a specific period, \(\bar{X}\) is the mean value of deforestation, \({Y}_{i}\) represent the values of sustainable livelihoods, such as the income or employment rate of the population in the region and \(\bar{X}\) is the mean value of sustainable livelihoods with a value \(r\) ranging from –1 to 1 respectively. To evaluate the impact of deforestation on the environment, and economic, cultural, and social livelihoods of the population at different time intervals, the Multiple Linear Regression (MLR) model was utilized for the study. The MLR has been widely used to evaluate multiple factors of deforestation affecting sustainable livelihood measures (Van Khuc et al. 2018 ; Soe and Yeo-Chang 2019 ). The MLR is calculated in Eq. ( 3 ).

where, \({Y}\) is the dependent variable (i.e., deforestation), which is a function of the linear combination of the independent variables \({x}_{1,}{x}_{2}\ldots {x}_{n}\) (e.g., environmental, economic, social, and cultural implications) and \({\beta }_{0}\) is the intercept, while \({B}_{1,}{B}_{2}\ldots {B}_{n}\) are the coefficients representing the change in \(Y\) for a one-unit change in each \(x\) and \({\varepsilon }_{i}\) is the error term.

Empirical Evidence and Analysis of Results

This analysis was carried out to analyze the causes and drivers of deforestation, its impact on social and economic livelihoods, and strategies for minimizing forest loss in the Ashanti region of Ghana (Asibey et al. 2020 ). The empirical evidence and analysis were computed by Eq. ( 4 ).

where, σ is the standard deviation, x 1 , µ is the mean, and N is the total number of respondents.

Demographic and Socioeconomic Status of Respondents

This paper explores socioeconomic and demographic factors to understand the drivers of deforestation in the Ashanti region of Ghana, using the above formula for computation. Table 1 shows a comprehensive overview of the demographic and socio-economic data of the respondents.

Table 1 shows the survey of 1000 respondents, 700, equating to 70%, were males compared to 300 or 30% females. The majority, 340 (34%) of the population, were between the ages of 31 and 40 years; this was followed by the ages of 18–30, constituting 24%, and the majority, 54% of the respondents, finished secondary and bachelor’s degrees. Most of the respondents (56%) were married, 21% were single, and 15 were divorced. One of the most striking findings showed that 42% of the respondents were unemployed, followed by 34% self-employed and 24% employed. The outcome of the survey further shows that 27% of the respondents were involved in cash crop production such as cocoa and palm trees; this is immediately followed by subsistent farmers of 26%, logging pit sawyers came out next at 20% while mining both small-scale and large scale were mentioned by 15% of the total respondents.

Long-term inter-annual deforestation trends in the Ashanti region from 2000 to 2020

Figure 6 shows the trends of deforestation in the Ashanti region of Ghana. The study used the MODIS vegetation indices time series product (MOD13Q1) from 2000 to 2020 to assess long-term inter-annual deforestation trends in Ghana’s forest areas since 2000. The results revealed an increasing rate of deforestation which became apparent in 2014 and 2020. The increasing trend can be attributed to the expansion of cities in the Ashanti region of Ghana. The trend line equation indicates an exponential increase in deforestation area over the years, with a value of 0.6438e 0.1874x square kilometers. The R ² value of 0.8197 indicates a strong correlation between the years and deforestation areas, confirming the accuracy of the trend line in the exponential model (Fig. 6 ). The deforestation trend in the year 2018 peaked at around 16,000 (Sqkm) which might be linked to the large-scale clearing of forests and trees for commercial purposes including exports to global markets. Similarly, an increasing trend of deforestation was reported and linked to the establishment of new communities and infrastructural development that support the growing population and economic growth (Puplampu and Boafo 2021 ). These authors disclosed that between 2008 and 2021, urban built developments have expanded from 55% to 84% at why the expense of the natural environment, including green spaces, which have declined from 41% to 15% over the same period. Puplampu and Boafo ( 2021 ) argued that forest zones are the major target for infrastructure development. Moreover, the state’s policies promoting oil exploitation, logging concessions, and hydropower dam constructions have both intentionally and unintentionally led to significant deforestation in various regions of the country. Consequently, technological advancements, both modern and primitive, have significantly impacted deforestation, with inefficient logging technologies causing further damage to forests (Yilmaz and Koyuncu ( 2019 )).

Of concern now is that forest crime and corruption have and continue to contribute to deforestation in Ghana (Cozma et al. 2023 ). The illegal logging and processing of forest raw materials across borders, facilitated by this phenomenon, has severe implications for deforestation in Ghana. In 2009, Ghana legalized informal small-scale logging under the European Union Forest Law Enforcement, Governance and Trade (FLEGT) and Voluntary Partnership Agreement, despite illegal forest activities being illegal in the country (Arts and Babili ( 2012 )). The FLEGT and VPA programs have successfully increased consultation and participation of the private sector and civil society in forest governance, thereby enhancing forest sector transparency (Weisse et al. 2022 ). However, these strategies have not effectively promoted forest governance reforms and motivated sector agents to actively participate in forest conservation and tree cultivation (Weisse et al. 2022 ). Moreover, the forest management project alienated local communities from ownership, reducing them to passive subjects with limited rights and influence, leading to an increase in small-scale illegal logging (Kyere-Boateng et al. 2021 ). Hence, the Ashanti region of Ghana experienced a significant increase that contributed to the exponential trend in deforestation activities from 2014 to 2020, attributed to various factors, as depicted in Fig. 6 .

Spatiotemporal analysis of vegetation cover and deforestation patterns in the Ashanti region

Understanding the deforestation trends in the Ashanti region and Ghana forms one of the key objectives of the research. This was achieved using the statistics of forest loss and integrating remote sensing and Geographical Information Systems in the region between 2000 and 2020. Figure 7a, b shows the spatiotemporal analysis of vegetation cover and deforestation patterns in the Ashanti region. The vegetation cover status at the start of the millennium shows that the year 2000 experienced dense vegetation cover in the northeastern and southeastern parts (Fig. 7a ). Most of the vegetation cover was sparsely dominated in the central while moderate vegetation was witnessed in the southern regions. It is worth noting that no vegetation was witnessed towards the southeastern parts of the study area. This connotes that the decline in tree and forest cover in this area might be due to the development of bare land, built-up, and/or urban areas. Nevertheless, dense vegetation cover dominated almost the entire region in 2000, with the most concentration in the southwestern parts of the Ashanti region (Fig. 7a ). This might be linked to the improvements in vegetation cover change by the government that was fully undertaken in Ashanti because the issues with the land use/ land cover change and over-cultivation were still at an early stage during this period. It is possible that this development, which the province government implemented, had an impact on the vegetation cover, especially in 2000. The occurrence, therefore, aligns with Sustainable Development Goal SDG15 (Life on Land), which aims to protect, restore, and encourage sustainable land use and practices (Liu et al. 2019 ; Schillaci et al. 2023 ).

a Spatiotemporal analysis of vegetation cover, and ( b ) deforestation patterns in the Ashanti region from 2000 to 2020

Additionally, deforestation trends in the Ashanti region between 2001 to 2005 show dense to moderate vegetation towards the central and southwestern parts (Fig. 7a ). The region in the northeastern parts was dominated by sparse vegetation to no vegetation in the area. This may result from moderated land use and land cover change associated with human activities such as logging, mining, and urbanization being put under more stringent regulations and guidelines (Emenike et al. 2020 ; Afuye et al. 2024 ). From 2006 to 2010, the area witnessed moderate to dense vegetation towards larger areas in the central, southeastern, and western parts, while the northeastern part was dominated by less dense vegetation. Concurrent with the vegetation cover, the years 2002, 2005, 2011, 2015, 2017–2018, and 2020 witnessed moderate to sparse vegetation and sparse to no vegetation in the northcentral part of the region. This may be attributed to intense human disturbances that might have contributed to the observed vegetation cover dynamics (Afuye et al. 2021a ). Additionally, the years 2011 to 2015 experienced a consistent trend and changes from moderate to sparse vegetation cover in the north and central parts. It is worth noting that 2018 witnessed moderate to sparse vegetation cover, and much of the changes were observed, while 2020 revealed dense to sparse vegetation in the southeastern parts of the Ashanti region. The observed built-up area became apparent in 2010, which may be due to the effects of arable farming, demographic pressure on land use change, and infrastructural development in the area (Koranteng and Zawila-Niedziecki ( 2015 ); Acheampong et al. 2019 ).

Figure 7b shows the spatiotemporal analysis of deforestation patterns in the Ashanti region between 2000 to 2020. Deforestation during 2000–2005 was primarily concentrated in northeastern areas with sparse vegetation cover, with some years showing more deforestation. From 2006–2010, deforestation patterns in northeastern areas with sparse vegetation cover expanded and intensified, affecting larger areas, indicating both expansion and intensification of deforestation activities. From 2011–2015, deforested areas became more widespread, with some regions experiencing higher concentrations. In previous decades, the dense to sparse vegetation was much noticeable in the central and northwestern areas, particularly when the land use changes were just in the early phase. From 2016–2020, deforestation in the region increased significantly, with a high peak in 2020, primarily in the southeastern parts, indicating a pervasive spatial distribution. The southeastern part was dominated by sparse to no vegetation as opposed to the previous years under investigation. The increasing gray areas indicate the expansion of deforested lands, underscoring the urgency of addressing deforestation and implementing sustainable land management practices (Fig. 7b ). Consequently, anthropogenic climate change exerts a significant influence on tree cover and forest cover change, which might have transformed the ecological landscape (Afuye et al. 2021b ; Wang et al. 2020 ). This development might have influenced the transformation of forest to other land, which could have impacted the soil structure associated with tree loss and forest cover change that occurred from dense vegetation to sparse in the region (Fig. 7b ). A comprehensive strategy for managing forest vegetation and mitigating drivers of deforestation under the concepts of ecosystem-based adaptation is needed to effectively address the challenges posed by environmental hazards in the country and any region (Busayo et al. 2022 ). The information-based model for sustainable ecological conservation and restoration policy must thus be included in comprehensive strategies to manage a range of activities that may be caused by both natural and human activities (Afuye et al. 2021a ).

Proximate causes of deforestation

This study aimed to identify the primary factors contributing to deforestation in the region. Respondents were asked to identify the main causes of deforestation in their communities. The responses of the respondents are depicted in Fig. 8a .

a Drivers of deforestation in the Ashanti region. b The underlying drivers of deforestation. Source: Field survey by Researchers

Cultural activities that contribute to deforestation in Ghana include shifting cultivation, fuelwood and charcoal production, weakening sacred grove protection, rituals, traditional land tenure systems, hunting, gathering, and unsustainable plant harvesting for traditional medicine (Boafo 2013 ; Fagariba et al. 2018 ; Butler, 2019 ; Asamoah et al. 2023 ). However, the majority of the respondents, 20%, mentioned agricultural expansion and logging and timber industry as the main and immediate causes of deforestation in their communities (Fig. 8a ). This was followed by fuelwood and charcoal producers at 14% and illegal chainsaw operations, and mining activities were mentioned at 13% and 10% respectively. This statistical breakdown was reinvigorated by a community leader who was engaged in an interview. This interviewee posited that:

“the enticements and the drive by government to farmers to go into large-scale production of cocoa, palm, and rubber have contributed to the clearing of large portions of forests to make space for the cultivation of these products. This interviewee further said it is cheaper to buy forest land and turn it into farmland in the Ashanti region than to invest in existing farmland to improve productivity and longevity”.

Another interviewee from the Ministry of Land and Natural Resources shared a similar perspective. This interviewee stated in an interview that:

“large scale and illegal logging activities for sawmill industries and other purposes are the primary contributor to deforestation in the country and Ashanti region in particular. According to this interviewee, logging operations employ clear-cutting; in some instances, entire sections of the forest are completely cleared off. These practices have resulted in massive deforestation in the region”.

Apart from the proximate causes, the study probed further into the underlying drivers of deforestation in the region. Respondents were asked to identify the root causes of forest loss in their communities. Figure 8b displays the answers of the respondents.

Most of the respondents (30%), attribute limited and no alternative to livelihood opportunities in their communities as the key driver to deforestation (Fig. 8b ). This is followed by pervasive poverty; this factor was mentioned by 26% of the respondents. Several respondents, 11% and 10% respectively, associated weak regulations, inadequate enforcement, and corruption among government officials as underlying drivers of deforestation in communities in the country and the region. Some interviewees buttressed these statistical outcomes. An employee from the Forestry Commission in Ghana said this during an interview:

“most of the rural or local communities in Ghana and Ashanti region lack any other income source besides clearing the forest or logging by legal or illicit means. This dependency on forest-related sustenance has contributed substantially to unsustainable deforestation”.

Another employee from the Regional Ministry of Land and Natural Resources expressed similar sentiments:

“the majority of the population in rural communities in the country are poor and have no alternative livelihoods; they rely mainly on forest resources for sustenance such as charcoal production, cutting of trees for fuelwood, hunting and gathering, illegal mining activities, illicit logging and subsistence in the form of slashing and burning. These collective activities contribute to deforestation as forests are overly exploited”.

An Environmentalist and an Advocator based in one of the communities expressed this during an interview:

“There is a lack of transparency in the Ghana forestry sector and this is contributing significantly to deforestation in all the regions in the country. This interviewee stressed that the opaque practices in the logging permits, land concessions, and timber harvesting have contributed to illegal and unsustainable deforestation. The lack of transparency has hindered any effort to enforce regulations and sustainable forest management in the country”.

A common perspective cutting across most of the respondents suggests that favorable markets for forest products from both domestic and global, weak regulations on forestry, lack of awareness, and complications of the land tenure system have collectively contributed to the escalation of deforestation in the region and Ghana as shown in Fig. 8b .

Implication of deforestation on the environment, social and economic livelihoods

This study evaluated the implications of deforestation on the livelihoods of the population. Respondents were asked to link the implications against a particular variable using a set of variables, with responses presented in Table 2 . The following equation was used to compile the ranking: [0 < x < 20 equal to (No implications); 21 > x < 100 (Less implications); 101 > x < 500 (Major implications); 501</ = 1000 (Very severe implications)]. Table 2 depicts the impacts of deforestation on the environment and livelihoods

Most of the respondents classified the twelve implications as either major or very severe implications on sustainable livelihoods (Table 2 ). For instance, 62% of the respondents are of the view that deforestation has contributed to the loss of jobs and unemployment, escalating poverty in the region and the country. Similarly, 69% mentioned that their health, water, and food security have been compromised due to high levels of deforestation. Interviews with some respondents buttressed the statistical breakdown. An interview with a community leader stated that:

“Our livelihoods depend on timber, non-forest products, ecotourism, and the hospitality industry. The decline in forest in our communities reduced attractions, limits the supply of timber which adversely affects employment for loggers, sawmill workers and other industry”.

The underlying views emanating from most interviewees suggest that deforestation in the region is multifaceted. Deforestation disrupts the water cycle, which compromises water quality and quantity, leading to the outbreak of diseases; it also disrupts the ecosystem and biodiversity, reducing agricultural production, food insecurity, environmental concern, and human well-being.

Mitigation strategies for deforestation in the Ashanti region and Ghana

Considering the negative implications of deforestation on the population’s livelihoods, this study explored strategies for mitigating the adverse effects of deforestation in the Ashanti region. Respondents were asked to pick their preferred strategies for addressing deforestation challenges from a set of variables (Fig. 9 ). Using a “Likert scale”, we calculated and classified respondents’ answers under the following variables: “Priority, Important Priority, and Very Important Priority”. Under this formula, we use a normal and ordering approach of (< less than; = equal to; > greater than; ≤ less or equal to ≠ not equal to; ≥ greater or equal to and x to equate numbers within a range) to calculate the answers provider. In this equation, responses were compiled as: [0 ≤ x ≤ 100 equal to “Long Term Priority”; 101 ≤ x ≤ 500 equate to “Medium Term Priority” and 501 ≤ x ≤ 1000 represent “Immediate/Urgent Priority”]. The responses are depicted in Fig. 9 .

Categorization of mitigation strategies for deforestation. Sources: Field survey 2023

Respondents classified legal reforms, sustainable logging reforestation, and afforestation, as well as community-based forestry, as immediate or urgent priorities that need to be enforced if the region and the country are to address the challenges of deforestation (Fig. 9 ). These were followed by promoting protected areas, encouraging alternative livelihoods, monitoring, and surveillance, and promoting education and awareness campaigns as medium-term priorities required in solving deforestation challenges. Other respondents grouped international cooperation, sustainable agriculture, research and innovation, and financial incentives as long-term objectives to be followed to solve deforestation problems in the country. These views were validated during interviews with some of the respondents. For instance, an interview with the director of the Ministry of Lands and Natural Resources posited that:

“legal reforms and strengthening existing laws such as land tenure rights, regulating logging and mining, encouraging community-based forest management, conducting effective environmental impact assessment. This includes improving forest monitoring with technology, harsh penalties for offenders and corrupt officials, and well collaborating with neighbouring and international agreements will go a long way to minimize deforestation activities in the region and the country”.

During an interview, an employee expressed a similar sentiment at the Forestry Commission of Ghana. This interviewee stated that:

“addressing deforestation activities in Ghana will require a holistic approach that embraces restructurings legal and regulations on forest management, responsible logging, community engagement, forest certification, corporate accountability, global cooperation as well as sustainable agricultural practices”.

A conservationist and Forester in the Ministry of Lands and Forestry said this in an interview:

“to slow the effect of climate change, conserve biodiversity, and impact the sustainable development goals, replanting and reforestation are critical. He stated that restored forests store carbon within the forest’s soil, shrubs, and trees. Mixed forests, for instance, are especially effective at carbon storage, as different species with complementary traits increase overall carbon storage”.

A common and underlying feedback emanating from the interviewees suggests that proactive and integrated strategies of robust legal regulations, active community involvement and ownership of forests, political will, and active educational and awareness campaigns will reverse the country’s deforestation trend.

Drivers and deforestation trends in Ashanti, Ghana

This study provides a comprehensive analysis of the drivers and trends of deforestation and its impact on Ghana’s environment and social and economic livelihoods. The findings of this study confirmed the views of many scholars, researchers, and commentators. From 2000 to 2020, the Ashanti region experienced relatively stable deforestation trends, with minor fluctuations, indicating a stable environment. From 2005, there was a noticeable increase, while between 2010 and 2015, there was variability with both increases and decreases. Post-2015, deforestation experienced a significant increase, peaking around 2018 and maintaining high levels through 2020. The Ashanti region is experiencing a rapid rise in deforestation, posing significant environmental challenges due to the pressure on forest resources and potential ecological degradation (Figs. 6 and 7a, b ). The results demonstrate a clear and concerning trend of increasing deforestation in the region, with an exponential growth pattern that underscores the urgency for sustainable forest management practices. In addition, the current trend emphasizes the need for effective forest management and conservation policies to combat deforestation, such as Koranteng, Zawila-Niedziecki ( 2015 ), Welsink ( 2020 ), Wimberly et al. ( 2022 ), and Afele et al. 2022 that deforestation trends in the Ashanti region have varied since the 1950s, however, a significant forest loss has occurred between 2000 and 2022. The geospatial analysis of the region suggests that since 2001, the region has experienced fluctuating trends in deforestation at an astounding yearly rate of forest loss, which has increased over the study period. Similar findings are expressed by Wimberley et al. (2022), who noted that from 1990 to 2000, the rate of forest cover loss in the Ashanti region was 3%; between 2001 to 2010, the rate was 2.6% and 1.5% and 2.5% between 2010 and 2020. This paper identified two main drivers as the main anthropogenic causes of deforestation. These include proximate or immediate causes and underlying factors. Agriculture expansion, logging, timber extraction, fuelwood and charcoal production, illegal chainsaw operations, infrastructure development, and traditional and cultural practices are the region’s proximate or immediate causes of deforestation activities. The case study findings by Andoh and Lee ( 2018 ) support the claim that deforestation in Ghana, particularly in the Ashanti region, is primarily caused by cash crop expansion, unsustainable logging, illegal mining, fuelwood burning, and infrastructure development. The outcomes of the study suggest that the underlying drivers play a significant role in sustaining the proximate causes (Fig. 8a, b ).

Furthermore, the study reveals that social, economic, and political factors such as poverty, weak regulations, corruption, land tenure systems, favorable international market conditions, and lack of education contribute to logging and deforestation. A similar outcome by Kyere-Boateng et al. ( 2021 ) discovered that while direct causes like agricultural expansion, logging, and mining are visible, underlying drivers like economic pressures, cultural circumstances, and political factors indirectly contribute to the expansion of deforestation in the region. Kissinger et al. ( 2012 ) argued that socioeconomic factors, bureaucratic licensing regimes, weak governance, political failures, corrupt officials, population growth, and global market forces all contribute to deforestation, leading to forest loss and environmental challenges. The findings from our engagements revealed a lack of focus on the underlying causes of deforestation, with many strategies focusing on addressing deforestation through stricter regulations or tree planting, without addressing poverty and lack of alternative livelihood strategies. Our engagements reveal that communities are largely excluded from forest management activities, despite conservation regulations and legislation recognizing communities and chiefs as forest custodians. The only way to protect and reduce deforestation activities in Ghana is by empowering stakeholders and ensuring their benefits from the forest and its products.

Implications of deforestation on sustainable livelihoods

Accordingly, deforestation has significant adverse impacts on the environment and sustainable livelihoods. The Pearson correlation coefficient was used to establish strong correlations between deforestation and sustainable livelihoods of the population in the region and country. The study reveals a significant negative correlation between deforestation and sustainable livelihoods, indicating that an increase in deforestation leads to a decrease in sustainable livelihoods (Table 2 ). Findings from Table 2 showed negative environmental impacts of deforestation, including loss of biodiversity, soil erosion, climate change, and reduced water and air quality. The study by Peprah et al. ( 2022 ) and Nyako et al. ( 2023 ) supports the notion that deforestation in Ashanti and Ghana results in significant environmental issues like biodiversity loss, habitat destruction, and climate change, thus affecting sustainable livelihoods. Studies uncovered negative impacts of deforestation that disrupts water cycles, reducing availability and quality, affecting air quality, and causing frequent vulnerabilities like flooding, droughts, and bushfires in urban areas further supporting our findings in Ghana’s Ashanti region (Acheampong et al. 2019 ; Amoah and Korle 2020 ; Sasu, 2022 ; Nyako et al. 2023 ; Asamoah et al. 2023 ). The findings of this study concurred with many commentators and researchers, such as Fagariba et al. ( 2018 ), Acheampong et al. ( 2019 ), and Siregar et al. ( 2023 ), that deforestation has far-reaching implications on economic livelihoods in the Ashanti region. The underlying findings in Tables 1 and 2 indicate that forest loss has contributed to the loss of jobs and employment opportunities, reduction in ecotourism destinations, reduction in agricultural production, and decrease in revenue for timber and non-forest products. The key findings in Table 2 reveal that the Ashanti region and Ghana’s economy heavily rely on agriculture, timber, and forest products, highlighting the direct impact of forest loss on employment, income, and livelihood activities. Furthermore, the extinction of forests in the region, due to their biodiversity, has reduced tourist interest, negatively impacting the hospitality and tourism sector, leading to reduced employment opportunities and business revenue. The findings of Yahaya et al. ( 2022 ) and Wale et al. 2022 report that deforestation in the region has led to significant job losses in the hospitality, travel, and service industries in local economies. Their study supports our findings on the significant social impacts of deforestation, including displacement, health risks, water and food security threats, cultural loss, and escalating conflicts in forest communities. Deforestation has led to the loss of traditional livelihoods, forced migration, health complications, and conflicts, particularly among youth, escalating vigilantism, and gangs, and threatening water and food security (Boafo 2013 ; Amoah and Korle 2020 ; Mulhneh 2021 ; Asamoah et al. 2023 ; Baffour-Ata et al. ( 2021 )). The underlying findings of this study suggest that the environmental, economic, and social implications have collectively impacted the stability and well-being of communities in the region and the country as a whole.

Intervention strategies in mitigating deforestation

This study explored strategies required to address deforestation challenges in the region and the country. The findings recognized that mitigating the problems of deforestation will require the amalgamation of immediate or short-term to long-term strategies, as illustrated in Fig. 8a, b . The findings from Fig. 8 and studies by Knoke et al. ( 2022 ) suggest that the region should implement legal reforms, sustainable logging practices, reforestation, afforestation, and community-based forestry as immediate or short-term strategies to combat deforestation. Our views and observations suggest that current legislation and policies governing forest management in the country are characterized by neo-liberal, government-centered approaches, disregarding the interests of farmers, landowners, communities, traditional authorities, and other stakeholders in forest governance and administration. Furthermore, The legislation and policies lack clear direction on sustainable logging, reforestation, and afforestation programs required to maintain forest resources. To combat deforestation, policy and legal reforms should enhance forest governance, promote sustainable logging, ban illegal activities, protect biodiversity, and implement community-based management. These reforms will enhance law enforcement and biodiversity protection. These findings align with den Besten et al. ( 2019 ) framework for addressing deforestation and forest degradation under the Reducing Emissions from Deforestation and Forest Degradation (REDD+) in developing countries including Ghana, stating that no single stakeholder can effectively eradicate deforestation activities in the country (Fig. 8a, b and Table 2 ). Therefore, effective surveillance and monitoring require collaboration between government agencies, the private sector, especially sawmilling companies, and local communities.

Conclusion and Recommendations

The study reveals that deforestation in Ghana’s Ashanti region is influenced by social, economic, political, and cultural factors, affecting the livelihoods of vulnerable communities and evaluating the impact of climate change. Additionally, the study analyzed vegetation cover and deforestation patterns from 2000 to 2020 using qualitative and space-based data. At the start of the millennium, the year 2000 experienced dense to moderate vegetation cover, while subsequent years witnessed a consistent decline in tree and forest cover. The study reveals a consistent decline in tree and forest cover quality from 2000 to 2013, exacerbated by landscape transformation response time in 2014, leading to a drastic decrease in vegetation quality. The study found a significant correlation between years and deforestation areas, particularly in 2018 and 2020, indicating an exponential increase with severe implications for sustainable livelihoods. Overall, between 2000 and 2020, the forest transformation showed a significant decrease in the quality of green vegetation conditions, from moderate to sparse. The findings of this study revealed a significant negative correlation between deforestation and sustainable livelihoods, indicating that an increase in deforestation leads to a decrease in sustainable livelihoods.

In light of the current trend of deforestation, poses significant environmental challenges due to the increasing pressure on forest resources and potential ecological degradation. Therefore, the exponential growth pattern underscores the need for sustainable forest management practices and effective conservation policies to combat deforestation. However, the Ghanaian government and traditional authorities have implemented various strategies and regulations to combat deforestation and anthropogenic climate change. Contrarily, the trends of forest loss are rather on the ascendency, particularly in the Ashanti region, indicating a growing environmental concern. The country’s forest management faces challenges such as siloed practices, community-driven strategies, corruption, poverty, and lack of alternative resources, exacerbated by environmental issues. The challenges at hand necessitate a holistic, integrated approach that includes addressing poverty, sustainable land use, and forest management. Promoting the use of trees like cocoa, coffee, and palm trees in farming can mitigate forest stress, maintain crop yields, and sustain the environment. The findings provide evidence-based and all-inclusive approaches to encourage marginalized groups to participate in policy and strategy co-production and co-creation. The outcome of this study is geared towards creating transformative and sustainable communities while ensuring efficient response and recovery capacities for deforested lands. Strengthening partnerships can be achieved by implementing innovative policies, upholding existing laws, and improving forest governance on both local and national levels. The goal is to improve forest conservation awareness and education, utilize technological advancements for efficient resource management, and meet population demands for social, economic, and livelihoods. Effective networked governance, including international organizations, the corporate sector, and local communities, is crucial for proactive actions and promoting sustainable agriculture.

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Inside the global effort to save the world’s forests

Today, more than 100 world leaders have promised to end and reverse deforestation by 2030 at the COP26 UN Climate Change Conference . The pledge , which includes almost $19.2 billion of public and private funds, is a landmark move for nature.

Research shows that a forest the size of Portugal is ripped from the earth each year, driving climate change and a host of other environmental crises, including wildfires, species extinction, and food insecurity.

A  2020 report from UNEP and the Food and Agriculture Organization found that, in the past 30 years,  420 million hectares of forest had been lost through conversion to other land uses (which is larger than the size of India), and that another 100 million hectares are at risk.

“Deforestation and forest degradation continue to take place at alarming rates, which contributes significantly to the ongoing loss of biodiversity,” the report stated. It warned that the Sustainable Development Goals would not be met by 2030 unless dramatic changes occurred in the agroforestry, agribusiness and agriculture sectors.

This critical issue has not gone unnoticed. For the last five decades, UN agencies, development institutions, governments, conservationists, the private sector and other key stakeholders have worked together to help protect the world’s forests, many of which are buckling under various pressures, including agriculture, resource extraction and illegal logging.

Working as a convener and catalyst, the United Nations Environment Programme (UNEP) has played an important role in supporting the global movement to slow deforestation, one that has made an impact everywhere from Vietnam to Mexico.

One innovative initiative, Reducing Emissions from Deforestation and Forest Degradation (REDD), has played a central role in combating climate change. The protection and restoration of forests is also tied directly to the current UN Decade on Ecosystem Restoration . The decade aims to prevent and reverse the degradation of ecosystems worldwide and is led by UNEP and the Food and Agriculture Organization.

“The growing enthusiasm for forests and trees is a good thing,” said Tim Christophersen , Head of UNEP’s Nature for Climate branch. “Ecosystem restoration will be critical in turning the tide against climate change and achieving the Sustainable Development Goals. The first rule for ecosystem restoration is to stop the further destruction of forests, wetlands and other critical green infrastructure.”

We have more and more evidence on action on the ground being effective. At the same time, we cannot be complacent.

 A long-running problem

https://www.youtube.com/watch?v=3LIwOEbOtAY

As far back as the 1970s, UNEP had been charged with crafting an international compact capable of halting deforestation. But deep political divides – the Global North largely drove forest development, while most major forests existed in the Global South – made a global accord unlikely.

So, UNEP broadened tactics, working with major development agencies, such as the World Bank, Food and Agriculture Organisation and the United Nations Development Programme, to combat deforestation on the ground.

UNEP is one of the three agencies constituting the UN-REDD Programme - the UN’s knowledge and advisory programme on forests and climate - and the largest international provider of REDD+ assistance. 

It has also championed the  Green Gigaton Challenge , an ambitious public-private partnership to catalyse funding to deliver 1 gigaton (1 billion metric tonnes) of emissions reductions by 2025 and annually after that.

“We have definitely seen a change in attitude at the leadership level,” said Mario Boccucci, head of the UN-REDD Secretariat. “Forest and nature-based solutions at large are more and more recognised as a key solution and constantly mentioned in global commitments. The private sector is also turning around, and a number of leading corporations and financial institutions are internalizing forest solutions in their response strategies.”

The growing enthusiasm for forests and trees is a good thing.

 Success stories

Some of the world’s most vulnerable forests have benefitted from UNEP support.

In Vietnam, shrimp farmers got help from UN-REDD in designing an organic farming model that helps to protect fragile mangrove forests. In Nigeria , UN-REDD programmes promoted forest management and biodiversity conservation, improving rural livelihoods. In Mongolia , UN-REDD is helping develop a national forest and climate change strategy focused on sustainable forest management. And in Colombia , local communities were brought into the conservation dialogue through UN-REDD supported workshops and training sessions.

UNEP has also pushed the international finance sector to provide real valuations for forest loss and to tie development to green standards and fair-trade practices.

“It is critical that the economics of ecosystem conservation becomes overwhelmingly compelling, “ said Gabriel Labatte, the UN-REDD Global Team Leader at UNEP. “Valuation of natural resources is important. However, it is more important to implement results-based payment mechanisms that make ecosystem conservation and restoration an attractive alternative.”

UNEP recently worked with the World Conservation Monitoring Centre to assess global rates of forest loss and joined with the Global Environment Facility, the World Bank, the International Union for the Conservation of Nature and six African nations to launch the $63 million Congo Basin Sustainable Landscapes Program in 2019. That is a key initiative, say experts, since Africa had the highest net loss of forest area from 2010 to 2020 and Africa’s rate of deforestation is on the rise.

Forest and nature-based solutions at large are more and more recognized as a key solution and constantly mentioned in global commitments.

A safe harbour

Forests cover approximately 31 percent of the global land area and provide habitat for the vast majority of the terrestrial plant and animal species known to science. Forests and the biodiversity they contain continue to be under threat from farming and exploitation, much of it illegal.

Large-scale commercial agriculture, such as cattle ranching and the cultivation of soya bean and oil palm, accounted for 40 percent of all tropical deforestation between 2000 and 2010, and local subsistence agriculture for another 33 percent. Forests also provide more than 86 million green jobs and an estimated 880 million people worldwide spend part of their time collecting fuelwood or producing charcoal, many of them women.

Forests are also key in the battle against climate change. They are essential stores of the carbon dioxide that is warming the planet, soaking up 30 percent of all emissions from fossil fuels and industry.

“We have more and more evidence on action on the ground being effective,” Boccucci said. “At the same time, we cannot be complacent as much more needs to be done. And that is where the UN, through its leadership, convening power and neutral broker capacities can continue to play an important role in the future.”

• Biodiversity

Further Resources

• Report: The State of the World’s Forests
• Video: Sir David Attenborough on forests
• Background: Forests
• Background: UN-REDD Programme
• Report: State of Finance for Nature
• Background: Investing in Forests to Build Back Better and Greener
• Report: Becoming #GenerationRestoration

Also part of this series

How the world is helping to safeguard its oceans and seas

Why the global fight to tackle food waste has only just begun

Inside the 20-year campaign to rid the world of leaded fuel

Related content.

Related Sustainable Development Goals

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Project impact assessment on deforestation and forest degradation: forest disturbance dataset

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ENCYCLOPEDIC ENTRY

Deforestation.

Deforestation is the intentional clearing of forested land.

Biology, Ecology, Conservation

Trees are cut down for timber, waiting to be transported and sold.

Photograph by Esemelwe

Deforestation is the purposeful clearing of forested land. Throughout history and into modern times, forests have been razed to make space for agriculture and animal grazing, and to obtain wood for fuel, manufacturing, and construction.

Deforestation has greatly altered landscapes around the world. About 2,000 years ago, 80 percent of Western Europe was forested; today the figure is 34 percent. In North America, about half of the forests in the eastern part of the continent were cut down from the 1600s to the 1870s for timber and agriculture. China has lost great expanses of its forests over the past 4,000 years and now just over 20 percent of it is forested. Much of Earth’s farmland was once forests.

Today, the greatest amount of deforestation is occurring in tropical rainforests, aided by extensive road construction into regions that were once almost inaccessible. Building or upgrading roads into forests makes them more accessible for exploitation. Slash-and-burn agriculture is a big contributor to deforestation in the tropics. With this agricultural method, farmers burn large swaths of forest, allowing the ash to fertilize the land for crops. The land is only fertile for a few years, however, after which the farmers move on to repeat the process elsewhere. Tropical forests are also cleared to make way for logging, cattle ranching, and oil palm and rubber tree plantations.

Deforestation can result in more carbon dioxide being released into the atmosphere. That is because trees take in carbon dioxide from the air for photosynthesis , and carbon is locked chemically in their wood. When trees are burned, this carbon returns to the atmosphere as carbon dioxide . With fewer trees around to take in the carbon dioxide , this greenhouse gas accumulates in the atmosphere and accelerates global warming.

Deforestation also threatens the world’s biodiversity . Tropical forests are home to great numbers of animal and plant species. When forests are logged or burned, it can drive many of those species into extinction. Some scientists say we are already in the midst of a mass-extinction episode.

More immediately, the loss of trees from a forest can leave soil more prone to erosion . This causes the remaining plants to become more vulnerable to fire as the forest shifts from being a closed, moist environment to an open, dry one.

While deforestation can be permanent, this is not always the case. In North America, for example, forests in many areas are returning thanks to conservation efforts.

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