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While deforestation is intended to increase economic productivity, its relationship with our standard of living is less conspicuous. This article inculcates how deforestation is related to economic achievement using empirical analysis and recent data, aiming to help contingent readers better fathom the topic and motivate them to take action. For policymakers, who are concerned about the impact of deforestation on economic growth or vice versa, the stated insights and evidence may be conducive to implementing related policies.

Deforestation Trends Around the World

Our World Data evinces the share of forest area to the total land across countries, as assessed by the UN Food and Agriculture Organization (FAO). Based on the available data, the average world forest cover during the five-year period 2015-2020 was approximately 31%. 

If we look at specific countries, however, things look different. In the case of Surinam, French Guyana, Guyana, Micronesia, and Gabon, for example, over 90% of forests were preserved over the same 5-year parameter. Still, most of the countries exhibit less than 50% of forest available across their territories, except for some nations that have greatly limited forest cover owing to their geographical condition. 

Among the Organization for Economic Co-operation and Development’s (OECD) 38 member countries, Finland is the nation with the highest forest retention, with a coverage of around 74%. Oppositely, Iceland figures at the bottom of the list, with 0.5% of forest at hand, despite a positive forest-restoring growth trend of 6.6% between 2015 and 2020. 

The tendency of afforestation or reforestation over the span of five years is also observed in Guam (+12%), Bahrain (+16.7%), and Malta (+31.4%). In comparison, forest loss has continued in Northern Mariana Islands (-17.4%), Oman (-16.7%), Israel (-15.2%), and Nicaragua (-12.8%). 

Why Are We Losing Forests?

Deforestation is primarily aimed at creating agricultural lands. The proclivity of forest clearance is closely related to the national economic status and development plan of a specific country. 

For developing economies, mowing down forests is indispensable to gain wealth, supplying agricultural and forestry products. In other words, deforestation can be seen as a direct conduit for economic growth. 

For advanced economies, however, forest clearance is not an obvious way of expanding income. Industrialised countries are more likely to experience technological progress and have solid infrastructure than developing economies, as they can count on advanced manufacturing industries, such as automobiles or heavy machinery, as well as service sectors, including health and education. Moreover, they have enough resources and capital to produce intellectual property products such as patents and software programmes, which further benefit the economy. 

The repercussion of deforestation is inimical to our environment, society, and economy. More specifically, biodiversity loss, poor air quality, soil destabilisation, water vulnerability, desertification, flooding, erosion, CO2 emission, and global warming are all, in some way or another, associated with forest loss. Besides, the local community living off forests will suffer from it. As a result, the development of local economies is likely impeded. To revitalise local markets, governments should take the responsibility of aiding local residents in settling into new circumstances by offering ephemeral pecuniary support. 

Deforestation affects not only our surrounding systems but also extant human beings and other species. For instance, the worsened environmental quality is more likely to influence human health conditions, both physically and psychologically, likely lowering labor productivity. This will end up having a negative impact on economic performance. Therefore, as an intelligent species, humans need to care about our forests and embrace other living organisms to enhance our quality of living. In order to do that, it is vital to understanding how deforestation is interrelated with our economy.

Research shows that numerous factors contribute to the increase or decrease of deforestation. Representatively, the extent of forest loss can be attributed to the dynamics of the labor market. If agricultural-intensive labour is more lucrative than other sectors in a nation, a surge of deforestation will occur. Meanwhile, empirical analysis bolsters that creating off-farm employment and increased remuneration in rural areas tend to reduce deforestation. This is because such discretionary employment opportunities induce more financial rewards compared to engaging in agricultural and forestry labor. 

The ever-growing world’s population is also mentioned as a major catalyst for deforestation because the desire of satisfying food demand and accumulating monetary assets

inevitably leads to the conversion of forests. A scholar claims that “increasing population will cause more pressure on forests.” On the other hand, however, while the world’s urban population reached around 56% in 2021 and is projected to keep growing in the future, the declining population in rural areas will likely undermine the force of deforestation, given that remaining rural lands have the potential to contribute to saving forest areas. 

The rise of trade openness, defined as the ratio of exports and imports to gross domestic product (GDP), is also assumed to have a crucial impact on exacerbating forest conservation, especially if the transaction of forestry and agricultural commodities is ratcheting up between countries. However, unlike the premise, Ririn Tri Ratnasari and co-researchers at the Department of Sharia Economics of Airlangga University in Indonesia behind an empirical examination found that international trade has nothing to do with deforestation for the case of 15 Organization Islamic Cooperation (OIC) countries – including Algeria, Indonesia, and Uzbekistan – over the period of 2010-2019. 

Empirical Evidence From Cross-Countries Analysis Over the Period 2010-2020

If we are able to postulate how a nation’s forest cover will change over time, it might be conducive for us to make a decision ahead to reduce any accompanying loss or maximise benefits. 

In the empirical analysis that follows – which endeavours to predict the change in forest cover employing cross-sectional data from 2010 to 2020 – the initial period of deforestation and economic growth are introduced as explanatory variables. In other words, the significance of beta convergence and environmental Kuznets curve (EKC) will be perused. 

The existence of so-called beta convergence will expound the change rate in forest areas. The concept of beta convergence is mostly applied in Economics studies, where the “per capita growth rate tends to be inversely related to the starting level of output or income per person. In particular, if economies are similar in respect to preferences and technology, then poor economies grow faster than rich ones.”

The bottom line is that an initially rich country will slowly grow while an initially poor country will grow rapidly.  In this case, the variable is only replaced, shifting the focus from gross domestic product (GDP) to a deforestation-related proxy. 

In 2020, Boka Stéphane Kévin Assa, Economic Policy Analysis Unite of CIRES (CAPEC) of University Felix Houphouet Boigny in Côte d’Ivoire, examined the relationship between deforestation and economic development for 85 tropical developing countries over the period of 1990-2010. According to his study, “beta convergence effects are also important in explaining changes in forest cover.

Following Assa’s research, more empirical analysis is needed to demonstrate whether the beta convergence effect is valid in the recent period spanning from 2010 to 2020. 

Due to the data availability, the sample period concludes in 2020. In this perusal, the short-run Green Solow model is deployed for estimation. A distinction from the original study is the omitting of additional control variables such as population density and institutional indicators, allowing for a more focused analysis of the deforestation proxy. The relevant data is garnered from two sources: UN FAO and World Bank. The forest cover is divided by the total population because a demographic effect needs to be taken into account when conducting a comparison between countries. 

Figure 1: Scatter plot created using data sources from UN FAO and World Bank. Graph: Goen Chang.

Figure 1: Scatter plot created using data sources from UN FAO and World Bank. Graph: Goen Chang.

Table 1: This table presents the result of the regression analysis . Notes: *** p0.01, ** p0.05, * p<0.10, Standard error is presented in parenthesis. All variables take log transformation. GDP per capita (2015=100, US$). Table: Goen Chang.

Table 1: This table presents the result of the regression analysis . Notes: *** p0.01, ** p0.05, * p<0.10, Standard error is presented in parenthesis. All variables take log transformation. GDP per capita (2015=100, US$). Table: Goen Chang.

Figure 1 describes the adverse relationship between the change rate and the initial level of forest cover per capita for 157 countries. This scatter plot graphically renders the tendency of beta convergence. In the second column of Table 1, the coefficient of the 2010 forest cover is found to be significant with a negative sign. Hence, the presence of beta convergence is empirically confirmed. This finding suggests that countries with lower levels of forest cover per person in the initial period are likely to experience higher growth in forest cover over the next period. The reverse trend is also predicted for countries with higher initial levels of tree cover. After all, countries are conjectured to converge toward a certain point in terms of forest cover per capita. The inclusion of proper independent variables would contribute to capturing forest cover dynamics effectively, enhancing the explanatory power of the investigation. 

But how exactly does deforestation is related to our economy? 

The environment Kuznets curve (EKC) construes a presumed relationship between environmental degradation (Y-axis) and GDP per capita (X-axis). Environmental degradation is measured by forest depletion, greenhouse gas emissions, air quality, etc.

The curve shows an inverted U-shape. Based on the hypothetical explanation behind the EKC, while low-income countries experience rapid growth, environmental degradation is increasing. However, once the economic status starts improving (in other words, when a country enters a division of relatively high-income countries), environmental degradation declines. 

According to a research on the economic causes of tropical deforestation, “with an increase in income, the structure of the economy and energy demand patterns might shift towards coal and petroleum-based fuels, thus reducing forest conversion pressures.” 

While this theoretical concept was popularised in the early 1990s, recent studies emphasise its inconsistency with real-world data and statistical insignificance. Rather, David I. Stern, a professor at Australian National University and prominent scholar on environmental matters, argues that “the true form of the emissions-income relationship is likely to be monotonic, but the curve shifts down over time.” 

This downward shape consistently appeared in the aforementioned study by Assa.

Figure 2: Scatter plot created using data sources from UN FAO and World Bank. Graph: Goen Chang.

Figure 2: Scatter plot created using data sources from UN FAO and World Bank. Graph: Goen Chang.

In addition to assessing beta convergence, the relationship between deforestation and economic growth is also appraised through the EKC theoretical framework. A basic EKC model is employed to run estimation over the period of 2010-2020. The deforestation rate is gained from the change rate of forest cover, multiplied by a negative one. Economic growth is measured by real GDP per capita (2015=100, US$) calculated by the World Bank.

In Figure 2, the scatter plot visualises the EKC for 157 countries. The inverted U-shape is not evident but the general trend shows a downward curve. Notwithstanding the obscure shape, the empirical appraisal confirms the validity of EKC. In Table 1, economic growth and its interaction term are all significantly observed with appropriate signs. The coefficient of economic growth (+1.293) infers that an increase in the growth rate of GDP per capita causes a higher rate of deforestation. However, once GDP per capita is approached around $2,672, deforestation is expected to tamp down as higher economic growth is attained, which is evidenced by the coefficient of the interaction term (-0.164). The addition of relevant control variables would improve the overall explanatory power of the model. 


In conclusion, empirical outcomes manifest that the initial period of forest cover and income per capita significantly influences the changes in forest availability. The further implication is that protecting more forests will be a boon in augmenting our standard of living and ensuring a healthy environment. To persist the mutual benefits, the role of individuals, related institutions, and government will be pivotal. Their responsibilities should be duly assigned and harmonised. Specifically, each individual is instigated to gain germane knowledge through self-education and have sedulous attention to the process of forest clearance. 

Any forest-related institutions need to decide based on accurate information and roll out innovative ideas, propelling forest protection movements. Government should also implement doable and transparent policies and be prepared for handling deforestation-related issues such as conflicts among various interest groups. 

You might also like: 10 Deforestation Facts You Should Know About


Andersen, L. E., & Reis, E. J. (2015). Deforestation, development, and government policy in the Brazilian Amazon: an econometric analysis (No. 69). Discussion Paper.
Assa, B. S. K. (2020). The deforestation-income relationship: Evidence of deforestation convergence across developing countries. Environment and Development Economics26(2), 131-150. 
Barro, R. J., & Sala-i-Martin, X. (1992). Convergence. Journal of Political Economy100(2), 223-251.
Febriyanti, A. R., Ratnasari, R. T., & Wardhana, A. K. (2022). The effect of economic growth, agricultural land, and trade openness moderated by population density on deforestation in OIC countries. Quantitative Economics and Management Studies3(2), 221-234.
Indarto, J. (2016). An overview of theoretical and empirical studies on deforestation. Journal of International Development and Cooperation. 107-120.
Kaimowitz, D., & Angelsen, A. (1998). Economic models of tropical deforestation: a review. 
Our World in Data. (2023). Deforestation and Forest Loss [Data file]. Retrieved from https://ourworldindata.org/deforestation.
Scrieciu, S. S. (2007). Can economic causes of tropical deforestation be identified at a global level?. Ecological Economics62(3-4), 603-612.
Stern, D. I. (2018). The environmental Kuznets curve. Companion to Environmental Studies49(54), 49-54.
UN Food and Agriculture Organization. (2023). Extent of Forest and Other Wooded Land [Data file]. Retrieved from https://fra- data.fao.org/assessments/fra/2020/WO/sections/extentOfForest/. 
World Bank. (2023). GDP per capita (constant 2015 US$) [Data file]. Retrieved from https://data.worldbank.org/indicator/NY.GDP.PCAP.KD. 
World Bank. (2023). Population, Total [Data file]. Retrieved from https://data.worldbank.org/indicator/SP.POP.TOTL.  
urban environmental sustainability; cycling; city; bike-friendly city

A city’s environmental sustainability can be measured using a vast set of indicators. Is it sensible to assess urban environmental sustainability with only a narrow set of these? After comparing various indicators across European cities, we conclude that some cities may perform well in one aspect but poorly in another and that cities must simultaneously consider many perspectives in order to ensure a truly environmentally sustainable future.


Cities that are best prepared for a “smart city future” should be sustainable, according to the recent 2022 ProptechOS report which benchmarks cities in the US and Europe on a selection of indicators. This is backed up by the Organisation for Economic Co-operation and Development (OECD) smart cities definition. In the report, sustainability is measured using a “green infrastructure” indicator across 46 European cities, combining:

This prompts the following questions: How do we measure the environmental sustainability of our cities? Do some cities perform well in one indicator but badly in others?

You might also like: What Is A Smart City?

How Do We Measure Urban Environmental Sustainability?

Although they correlate with lower emissions, the numbers of EV charging points and green-certified buildings measure environmental progress in a (current) model of urbanisation centred around cars and buildings. 

According to the UN Habitat World Cities Report 2022, it is important that the goal for a future city should place environmental sustainability at its core, however, we must consider a multitude of desirable outcomes to ensure that any solution benefits the planet and its inhabitants. As an example, electric cars can play only one part in a wider approach to sustainable urban mobility.

The European Environment Agency (EEA) report presents an urban environmental sustainability framework that pulls together many different building blocks for a common goal, described through the lenses of a city that is green, low-carbon, resilient, circular, inclusive and healthy. We will compare ProptechOS’ “green infrastructure” indicator with the following factors:

How Do Different Measures Compare?

Other organisations have recently published indicators measuring the above urban environmental sustainability factors in cities around Europe. We look at how some of these published indicators compare to those published by ProptechOS.

We include 6 indicators published in the 2019 SDG Index and Dashboards Report for European Cities. These measure subsets of the UN Sustainable Development Goals (SDG) linked to urban environmental sustainability, originally across 45 European cities. We also include 3 indicators published in the Clean Cities Campaign 2022 rankings. These are transparently and robustly researched indicators prioritising zero-emissions mobility, originally across 36 European cities.

Click the dropdown to view the indicators. See Appendix below for descriptions of all indicators used.

Note that this is not designed to be a complete analysis of all European cities, and many originally studied cities have been omitted because of lack of data across datasets. To reproduce this analysis, see the code.


How do each of these indicators compare to the ProptechOS measure of green infrastructure?

1. Climate policies

We also observe a very strong positive trend, showing that the development of certain infrastructure is associated with strong climate-positive policy decisions with regard to other infrastructure, with cities such as London (United Kingdom) and Amsterdam (Netherlands) performing highly and Warsaw (Poland) and Prague (Czech Republic) performing poorly on both indicators.

2. Access to climate-friendly mobility and space for people

We notice a weak positive trend with notable outliers, showing that in some cities, sustainable mobility covers much more than just electric vehicles. For example, London (UK) is known for being poorly cyclable and suffers from high congestion, whereas Copenhagen (Denmark) has the most affordable public transport and is widely ranked among the most cyclable cities in the world.

3. SDG15: Life on land

This indicator measures the quality of natural habitats and the provision of green space in cities. We see an interesting positive and negative trend. Cities like Ljubljana (Slovenia) and Zagreb (Croatia) are at the top of SDG15 despite being lower on the Green Infrastructure scale, whereas cities considered more highly developed such as Amsterdam (Netherlands) or London (United Kingdom) perform worse in SDG15, showing a higher negative impact on the environment and ecology. One notable exception is Oslo (Norway), which is notably prioritising biodiversity in the built environment as a rapidly expanding metropolitan area.

4. Recycling rate

We see a fairly strong positive trend, showing that cities prioritising green infrastructure, such as Berlin (Germany), are also progressive in waste management.

5. Air quality (concentration of particulate matter, PM2.5)

We see cities clustered into groups. A few major cities such as Berlin (Germany), Amsterdam (Netherlands), London (UK) and Paris (France) have highly developed infrastructure but moderate air pollution as measured by the concentration of PM2.5 in the air. Scandinavian cities, along with Madrid (Spain), Lisbon (Portugal) and Dublin (Ireland) perform well in terms of air quality. Predominantly Eastern European cities form the group suffering from the poorest air quality.

You might also like: Sustainable Cities: The Example of Gothenburg


From the above analysis of various indicators covering infrastructure, energy, and nature, we see that urban environmental sustainability must be measured in a wider framework to capture a more meaningful indicator of urban environmental sustainability, which, as the EEA suggests, must act as a foundation of future cities. 

For example, London has a high density of certain green infrastructure and performs well in some related measures, but not so well in others. Notably, its efficient public transport system must be coupled with better cyclability and lower congestion. Paris, with its strong climate-positive policy directions, needs to additionally tackle its low score regarding environmental and ecological quality as part of SDG15.

Of course, every European city is at a different stage on the journey towards fully sustainable development, and each city faces unique challenges in tackling intertwined issues in environmental, social, and economic sustainability. However, we cannot truly laud a city as environmentally sustainable unless it has measured and addressed all components, requiring a shift away from a narrow-scoped model of sustainable urbanisation.


In using the following indicators, we assume that cities are defined in the same way across datasets and that there has been little change between 2019 and 2022.

2019 SDG Index and Dashboards Report for European Cities indicators. We select a few indicators covering SDGs 7, 11, 12, 13, and 15. Further information is available here.

Clean Cities Campaign 2022 rankings indicators. Further information is available here.

Note that individual indicators have limitations. It is difficult to standardise a vast number of metrics that may be measured differently across the continent in order to provide a comprehensive analysis. Furthermore, a comprehensive analysis should also study more complex cross-sectoral effects. Future analyses could compare further indicators such as raw energy and water usage and wastage, further per capita measures, risk measures such as flooding risk, and quantification of effects such as the urban heat island effect.

Featured image by Martin Magnemyr on Unsplash

You might also like: How Sustainable Cities like Singapore Succeed in Green Urban Development

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