Annual Review 2023

I've been doing these annual reviews since 2011. They're mainly an exercise for me to see what I accomplished and what I didn't in the previous year. 

The big new development this year is that together with Johannes Sauer and Eric Yu Sheng I took on the joint editor-in-chief role of the Australian Journal of Agricultural and Resource Economics. My goal, apart from the usual ones of increasing the impact, prestige, and outreach of the journal, is to get more environmental and resource economics papers published in the journal. So, if you have a suitable paper, please send it to us! We started handling new manuscripts in July and from 1 January we are officially the editors, though we have been increasingly taking on the various roles over the last few months. I will be at the AARES Conference in February, giving a report at the AGM and hosting a "Meet the Editors" session.

I made three trips out of the Canberra and NSW coast region this year, the first since early 2020. I still  haven't been outside of Australasia since 2018. It's pretty hard to travel for any length of time without taking the family and with two small children neither of us feels like traveling anywhere very far. I flew twice to Melbourne for conferences. In March for the Western Economic Association Conference and at the end of November for the Monash Environmental Economics Workshop. I presented our research on electricity markets at both of them.

The third trip was in January to Sydney for a holiday. There is a bit more activity on campus than last year, but it is still much quieter than before the pandemic. For example, we lost three food outlets near Crawford during the pandemic, which haven't returned.

Coogee, NSW

This was the second year that I taught IDEC8018 Agricultural and Resource Economics. Though it was a lot easier than last year, I still had to do quite a lot of preparation for this course. I taught it together with IDEC8053 Environmental Economics in Semester 2. I last taught IDEC8053 in Semester 1, 2021. I taught lectures in hybrid mode – an in-person lecture livestreamed on Zoom. Tutorials were split between an in-person and an online tutorial. The courses went OK, but attendance both online and in-person fell off sharply as the semester progressed. Michael Ward told me that at Monash Economics they are dropping live lectures and switching to pre-recorded material only together with in-person workshops.

It was a slow year for research. I worked on some things that went nowhere, did a couple of projects with visitors, inched a few other things along, and discussed funding proposals. At the end of the year I was working on modelling glacial cycles, which I am trying to give up on, and completing a new paper with Xueting Jiang that hopefully we can post early next year.

I had a couple of visitors to Crawford during the year. Suryadeepto Nag visited Crawford to work with me on his master's project, which I jointly supervised, from late November 2022 to February 2023. We researched the impact of electrification on development in rural India using Indian survey data. The paper has just been rejected by one journal and we are now revising it to give it another shot elsewhere. The editor and referees didn't seem to get the propensity score weighting approach to addressing selection bias.

In July, Khalid Ahmed visited. We wrote a piece on recent developments in Chinese carbon emissions for The Conversation. We managed to publish an updated version in a new journal called Environmental Challenges. Khalid has now moved to Brunei.

I published four journal articles with a 2023 or in press date:

Timilsina G., D. I. Stern, and D. K. Das (in press) Physical infrastructure and economic growth, Applied Economics. 

Ahmed K. and D. I. Stern (2023) China's carbon emissions trend after the pandemic, Environmental Challenges 13, 100787. 

Jiang X. and D. I. Stern (2023) Asymmetric business cycle changes in U.S. carbon emissions and oil market shocks, Climatic Change 176, 147. 

Kubiszewski, I., L. Concollato, R. Costanza, and D. I. Stern (2023) Changes in authorship, networks, and research topics in ecosystem services, Ecosystem Services 101501. 

The first paper was already in press in 2022 and the last was accepted in 2022 as well. There are also a couple of book chapters.

We posted three new working papers:

Are the Benefits of Electrification Realized Only in the Long Run? Evidence from Rural India. July 2023. With Suryadeepto Nag.

China's Carbon Emissions After the Pandemic. July 2023. With Khalid Ahmed.

More Than Half of Statistically Significant Research Findings in the Environmental Sciences are Actually Not. January 2023. With Teshome Deressa, Jaco Vangronsveld, Jan Minx, Sebastien Lizin, Robert Malina, and Stephan Bruns.

We have one journal article under review at the moment. This is a second submission of our paper on confidence intervals for recursive impact factors. We are also working on a revision of the paper authored by Deressa et al. mentioned above and the revision of the Indian electrification paper. There are several other papers on my to do list, but they range from one we are actively trying to complete, to ones that I haven't really done anything on any time recently and ones that may never happen.

Google Scholar citations reached roughly 26,000 with an h-index of 61. I again wrote fewer blogposts this year. Five in total compared to eight in 2022. Twitter followers rose from 1750 to almost 1850 over the year. People keep talking about the demise of academic Twitter. Maybe there are fewer academic posts than before, but unclear if there is one  single other place where people are congregating. I find Twitter very useful for news and don't want to spend time trawling various platforms looking for content.

I reviewed 8 journal articles, two tenure or promotion cases, one book proposal, and two grant proposals, one promotion case, and one textbook proposal. I am taking on fewer reviews because of my new role as AJARE editor. So, I turn down quite a lot of journal article and some grant review requests. I prioritize journals that I have published in or have been reviewed by recently.

My PhD students Xueting Zhang and Debasish Das both submitted their PhDs in the second half of the year. I took on a new PhD student, Mi Lim Kim, who is working on supply-side climate policy. I also have a new student, Banna Banik from Bangladesh, starting in early 2024.

I am not going to make any more predictions this year, because some of last year's predictions did not materialize!

My Climate Change Policy Assumptions and Expectations

Matthew Kahn posted a list of his working assumptions on climate change. I think it is really enlightening to see these laid out rather than just expressed implicitly. So I thought I'd list my ideas in response to each of Matt's points. In the following, Matt's points are in bold and mine in plain text.

1. I believe that global GHG emissions will continue to rise for decades. 

Technological change in non-carbon emitting energy technologies has been surprisingly fast despite climate policies having been relatively weak. This makes me optimistic that emissions will soon begin to fall. We used to talk about steeply rising emissions paths like RCP 8.5. In the most recent IPCC report, business as usual is now a fairly flat emissions path (not that we should put too much weight on consensus). On the other hand, I am pessimistic on energy intensity falling by as much as is assumed in many integrated assessment models (IAMs).  So, my expectation is for some fall in emissions or at least a flat path till 2050. I don't expect a steeply declining path because so much fossil fuel infrastructure continues to be built. My best guess is that we will somewhat overshoot the 2ºC target but in the later part of this century we will get really serious about carbon sequestration, which will eventually bringing the temperature down again. If we are lucky, impacts will remain fairly linear and we will avoid tipping points.

2. I do not take integrated assessment models of the impact of climate change seriously.

In general, I agree. On both the impact and technological change sides they are mostly just speculation, particularly on the impacts side. On the other hand, having some idea of how much we need to cut emissions at what cost is useful... and they can generate the social cost of carbon (see below). 

One of my standard assumptions is that technological change in terms of increasing technical efficiency of production will eventually end. It's likely that the level of technology will follow a big S shape curve from the Industrial Revolution on, and we are somewhere near the middle of the curve right now.

3. /4. Urbanization increases one’s income as one acquires more skill to succeed in the urban market. Private income growth fuels adaptation as people have more resources to protect themselves from the serious threats we now face.

Urbanization is part of the development process that increases energy use and to date carbon emissions but also provides some more adaptation capacity though it reduces other abilities to adapt. Density reduces the overall need for transport and for heating but increases the need for cooling. So overall I don't have a strong opinion on urbanization.

5. Due to market innovation, I believe that the Social Cost of Carbon (SCC) will actually decline over time.

The resource scarcity literature teaches us that the expectation that the efficient path of a price of a non-renewable resource is simply to grow at the discount rate as in the simplest Hotelling model isn't necessarily true. And if we solve the climate problem, then maybe the SCC will come back down again. I say "maybe" because though carbon in the atmosphere might be falling, we will have more to protect from impacts? In the long run, the carbon sink isn't a non-renewable resource. However, in the near term it seems reasonable to expect that the SCC is rising. Of course, the SCC is just an estimate, which is either generated by an IAM or depends on the same assumptions as an IAM. On the other hand, as long as we don't have an effective carbon price, we need a social cost of carbon number to put in cost benefit analyses.

6. The proper role of government here merits much more research. When do government efforts protect the poor versus when do government investments and rules create moral hazard and “Peltzman” effects such that we take on more risks such as moving to a risky area that the government has invested in sea walls to protect?

I think the government needs to take climate change into account when planning and adapting public infrastructure. And it has an important role in providing people information about climate change. But beyond that I don't see it has a role in adaptation. People bear the costs of adapting privately. I don't see a market failure there except due to information. So, I don't know why this should get specific attention rather than just be a side effect of general social welfare policy. Should we be building sea walls to protect land from flooding and is that a coordination problem? Well, I can't see how that can be anything but a short-term solution and so probably we shouldn't.

7. I am a fan and a producer of reduced form climate correlations. For example, over the last 4 decades how much lower has the growth rate of a nation’s per-capita income been during years when it very hot? These correlations are interesting. They play a “Paul Revere” role teaching us what future costs we could bear if we fail to adapt.

I am not a fan of this literature. I think it is more or less meaningless regarding climate change in general. If there is a one time hot year, you are not going to do long-term adaptation as Matthew points out. On the other hand, long-term impacts of climate change like sea level rise and species extinction won't happen due to one hot year. The literature can tell us something about what will happen if there are more of these exceptional years in the future but that's about it in my opinion.

Are the Benefits of Electrification Realized Only in the Long Run? Evidence from Rural India

 

I have a new working paper coauthored with my master's student Suryadeepto Nag on the impact of rural electrification in India. Surya did his master's at IISER in Pune with me as his supervisor. He visited Canberra over the last Southern Summer. This paper is based on part of Surya's thesis.

The effect of providing households with access to electricity has been a popular research topic. It's still not clear how large the benefits of such interventions are. Is electricity access an investment that generates growth? Or is it more of a consumption good that growing economies can afford? Researchers have used traditional econometric methods on secondary data (observational studies) and also carried out field experiments, such as randomized controlled trials (RCTs), to try to answer this question.

Experimental methods have generally found smaller and less statistically significant results than observational studies have. Is this because experiments are more rigorous? Or because observational studies usually measure impacts over a longer period of time? It's likely that it takes time for people to make use of a new electricity connection. They will need to save and buy appliances. Effects on children's education will take an especially long time to come to fruition.

We carry out a meta-analysis of 16 studies previously reviewed by Bayer et al. (2020):

We assigned each positive impact (for example on income or on education) found in a study the score of 1 and each negative impact a -1 and then averaged over all the impacts. The graph shows this "positiveness of impact" compared to the time households had been connected to electricity. While observational studies found more positive impacts than experimental studies, there is also a positive correlation between duration of connection and positiveness of impact (and between duration of connection and being an observational study). Regression analysis shows that only duration of connection is statistically significant. 

But this small sample of studies can't be that conclusive, so we then carry out our own analysis to test whether impacts increase over time.

Using three waves of Indian household surveys from 1994-95, 2004-5, and 2011-12, we quantify the impacts of short-term (0-7 years) and long-term (7-17 years) electricity access on rural household well-being. These surveys tracked the same households over time. We don't know exactly when a household was connected, just whether it was already connected in 1994-95 or whether it got connected between the other surveys. We do know when villages were connected.

We use a difference in differences regression that is weighted using "inverse propensity scores". This is supposed to compensate for the fact that households are not actually connected randomly to the grid. If, for example, poor households are less likely to get connected, we overweight them in the sample. In our main analysis, we exclude households that were already connected in 1994-95 so that the control group only includes households that were not connected by 2011-12.*

We find that long-term electricity access increases per capita consumption and education, and reduces the time spent by women on fuel collection (compared to the control group). The effect of short-term connection is smaller and statistically insignificant. We find no significant effects on agricultural income, agricultural land holding, and kerosene consumption. 

Here is our main table of results:

The long-term impact on consumption is really very big – 18 percentage points more than the control group over a 7 year period. The effect on education is 0.4 of a year relative to the control group.

We did some robustness tests – using different weights and including the households connected before 1994-95 as "very long-term connections". The results roughly hold up, though the weighting isn't ideal in either case.

We think our results show that experimental studies really need longer term follow-ups before coming to conclusions.

* The recent research on differences in differences shows that many past studies used inappropriate control groups.

China is pumping out carbon emissions as if COVID never happened. That’s bad news for the climate crisis

David Stern, Crawford School of Public Policy, Australian National University and Khalid Ahmed, Australian National University

Carbon emissions from China are growing faster now than before COVID-19 struck, data show, dashing hopes the pandemic may have put the world’s most polluting nation on a new emissions trajectory.

We compared emissions in China over the first four months of 2019 – before the pandemic – and 2023. Emissions rose 10% between the two periods, despite the pandemic and China’s faltering economic recovery. Power generation and industry are driving the increase.

Under the Paris Agreement, China has pledged to ensure carbon emissions peak by 2030 and reach net zero emissions by 2060. Our analysis suggests China may struggle to reach these ambitious goals.

Many believed the economic recovery from COVID would steer global development towards a less carbon-intensive footing. But China’s new path seems to be less sustainable than before. That’s bad news for global efforts to tackle climate change.

 

China has pledged to ensure carbon emissions peak by 2030 – but it’s heading in the opposite direction. Olivia Zhang/AP

An alarming trend in emissions

The COVID pandemic curbed greenhouse gas emissions in 2020, largely due to a drop in passenger travel. This led to hopes of a “green” economic recovery in which government stimulus spending would be invested into climate-friendly projects, to ensure a longer-term slowing of growth in emissions.

Some researchers examined the trends in China’s emissions up to 2019 and predicted the nation’s emissions would peak by 2026. Others have said the peak will occur even earlier, in 2025.

But unfortunately, it seems those predictions were too optimistic.

We examined data from Carbon Monitor, which provides science-based estimates of daily CO₂ emissions across the world. We compared emissions data from January to April 2019 (which represents typical pre-pandemic conditions in China) with the corresponding months in 2023. This period followed the removal of most COVID-related restrictions in China – such as testing requirements and quarantine rules – which essentially restored the country’s economy to business-as-usual.

We found average daily carbon emissions increased substantially between the two periods. In the first four months of 2019, China’s transport, industry, energy and residential sectors together emitted an average 28.2 million tonnes of CO₂ a day. In the first four months of 2023, daily emissions from those sectors were an average 30.9 million tonnes.

Emissions from the residential and transport sectors didn’t change much. This is mildly good news – it’s better than emissions going up. But these are the two smallest sectors, together accounting for only 18% of China’s emissions.

Rather, the increase was driven by emissions from China’s industrial and energy sectors. Average daily emissions from industry rose between 2019 and 2023 by 1.1 million tonnes or 11%. From energy, which includes electricity generation, they rose by 1.75 million tonnes or 14%.

Energy production from solar and wind in China did increase substantially between the two periods. But the growth was outweighed by electricity generated from fossil fuels.

Graph showing energy generation mix in China in the first four months of both 2019 and 2023. National Bureau of Statistics of China

Separate data show the growth of coal production in China has accelerated. In the two years prior to the pandemic, coal production variously fell or only grew slightly. But coal production grew during the pandemic, and this has continued. In the year to April 2023, coal production increased by about 5%.

While coal’s share of energy consumption fell substantially from 2007 to 2019, it has changed little since then. That’s mainly because energy use is growing fastest in the electricity sector, which remains dominated by coal.

The global picture

Emissions in many developed countries have fallen in recent years due to government policies, slow economic growth, and the shift from coal to natural gas.

Developing nations increasingly dominate global emissions. China might be expected to be a leader on the clean energy shift among developing countries – in part because it produces much less oil than it consumes. That means its energy supply is not secure, giving it an incentive to find alternative sources of power.

There’s another reason why China should be a trailblazer on emissions reduction. China is the world’s biggest emitter – so a percentage reduction in emissions there leads to far fewer tonnes of CO₂ in the atmosphere than if a smaller country reduced emissions by the same percentage. And, partly because China’s population and economy are so big, it stands to benefit more than any country in the world from a more stable global climate.

But as we’ve outlined, China’s trajectory is by no means world-leading. What’s more, moves by China on the international stage suggest it’s becoming less cooperative in climate negotiations than in recent years. We saw this at the COP27 global climate conference in Egypt late last year, when China did not join a pledge to curb methane emissions and refused to provide financial support to developing nations vulnerable to climate change.

The potential for cooperation on climate policy is being reduced further by ongoing tensions between China and the United States. All this serves to cast doubt on China following through on its Paris pledges – and certainly, on any chance its emissions will peak in the next two years.

David Stern, Professor, Crawford School of Public Policy, Australian National University and Khalid Ahmed, Visiting Fellow, Australian National University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Video from Arndt-Corden Seminar

Today, I gave a seminar in the Arndt-Corden Department of Economics Seminar Series, titled: "Electricity Markets with Speculative Storage and Stochastic Generation and Demand." We hope to post a working paper soon. In the meantime, here's the video * of my seminar: 

 * Introduction and question time deleted

Changes in Authorship, Networks, and Research Topics in Ecosystem Services

I have a new paper, coauthored with Ida Kubiszewski, Bob Costanza, and Luke Concollato, which investigates the development of the field of ecosystem services over the last decade since the founding of the journal Ecosystem Services. This is an open access publication – my first in a so-called hybrid journal. We used the University College London read and publish agreement with Elsevier to publish the paper. ANU now has a similar agreement starting in 2023.

The paper updates Ida and Bob's paper published in 2012: "The authorship structure of ‘‘ecosystem services’’ as a transdisciplinary field of scholarship". In this paper, we update and expand that analysis and compare results with those we found in the previous analysis. We also analyse the influence that the journal Ecosystem Services has had on the field over its first 10 years. We look at which articles have had the most influence on the field (as measured by the number of citations in Ecosystem Services) and on the broader scientific literature (as measured by total citations). We also look at how authorship networks, topics, and the types of journals publishing on the topic have changed. 

Not surprisingly, there has been significant growth in the number of authors (12,795 to 91,051) and number of articles published (4,948 to 33,973) on ecosystem services since 2012. Authorship networks have also expanded significantly, and the patterns of co-authorship have evolved in interesting ways. The most prolific authors are no longer in as tight clusters as they were 10 years ago.

The network chart shows the coauthorship relations among the 163 most prolific authors – those authors who have published more than 30 articles in the field. Colors indicate continent: Yellow = North America, red = South America, blue = Europe, purple – Africa, green = Asia, and orange = Oceania. The greatest number of authors is in Europe and they almost all collaborate with other top authors. Only in Asia and to a lesser degree North America are there top authors who do not collaborate with other top authors.

Costanza et al. (1997) is the most influential article in terms of citations in the journal Ecosystem Services and "Global estimates of the value of ecosystems andtheir services in monetary units" by de Groot et al. (2012) is now the most cited article published in Ecosystem Services.

Ecosystem Services is now the most prolific publisher of articles on ecosystem services among all the journals that have published in the area. There are nine journals that are both on the list of the 20 journals cited most often in Ecosystem Services and on the list of the top 20 journals cited by articles published in Ecosystem Services: Ecosystem Services, Ecological Economics, Ecological Indicators, Science of the Total Environment, Land Use Policy, Journal of Environmental Management, PLoS One, Ecology and Society, and Environmental Science & Policy.

Annual Review 2022

I've been doing these annual reviews since 2011. They're mainly an exercise for me to see what I accomplished and what I didn't in the previous year. 

 

This was the second year since I have been back living in Canberra in 2007 that I spent the entire year in the Canberra region (extending to the coast) and the second year since 1991 that I didn't fly on a plane. I also haven't been outside of Australasia since 2018. It's pretty hard to travel for any length of time without taking the family and with two small children neither of us feels like traveling anywhere very far. I think we can finally say that the pandemic is over when ANU finally lifted their mask mandates in late October. The university seems pretty dead post-pandemic outside Kambri at lunchtime. Some of our students are still stuck in China etc. but even people who are in Canberra have been reluctant to show up. Graduation week in mid-December showed a flurry of activity.

Kambri

For the first three months of the year, I was supposedly on long service leave (LSL). This might sound good, but actually it is a sacrifice of close to AUD 50k that I would otherwise have been paid out when I retired. I did it to help the university's budget... In return I only taught one course this year. But I ended up agreeing to teach a course that was new to me: IDEC8018 Agricultural and Resource Economics. This turned out to be a huge amount of work in terms of preparation. I started working on the course in February. So, I'm not sure I gained much from my LSL. The main benefit is that I have now managed to move my teaching to Semester 2, which I think is better.  I taught lectures in hybrid mode – an in-person lecture livestreamed on Zoom. Tutorials were split between an in-person and an online tutorial. I think the course went well given it was the first run and there were various hiccups along the way. The teaching evaluations are strong.

 

The main new research I did this year was our paper on confidence intervals for recursive journal impact factors. This research followed up on my 2013 Journal of Economic Literature which computed standard errors and confidence intervals for journal impact factors. Back then, I speculated that confidence intervals could be computed for recursive impact factors and now we've done it. As usual, various collaborative research projects are in progress. Some are mentioned on my research page. I also worked with Ida Kubiszewski and Bob Costanza on a paper on the field of ecosystem services, which we have already published.

I only published two papers with a 2022 date:

Berner A., S. Bruns, A. Moneta, and D. I. Stern (2022) Do energy efficiency improvements reduce energy use? Empirical evidence on the economy-wide rebound effect in Europe and the United States, Energy Economics 110, 105939.

Jafari M., D. I. Stern, and S. B. Bruns (2022) How large is the economy-wide rebound effect in middle income countries? Evidence from Iran, Ecological Economics 193, 107325.

and one with a 2023 date: 

Kubiszewski, I., L. Concollato, R. Costanza, D. I. Stern (2023) Changes in the authorship, networks, and research topics in ecosystem services, Ecosystem Services 101501.

We have one in press paper:

Timilsina, G., Stern, D. I., and D. Das (in press) Physical infrastructure and economic growth, Applied Economics.

Following ANU signing read and publish agreements with Elsevier and Taylor and Francis among others, these will be my first open access articles in hybrid journals.

We only posted one new working paper:

Confidence Intervals for Recursive Journal Impact Factors. June 2022. With Johannes König and Richard Tol.

We have two journal articles under review at the moment. There are a lot of other papers on my to do list, but they range from one we are actively trying to complete, to ones that I haven't really done anything on any time recently and ones that may never happen.

I gave a couple of online conference and seminar presentations. The first was in March in the FEEM Economic Modelling Seminar Series on the topic of Asymmetric Response of Carbon Emissions to Changes in GDP and Negative Oil Market Shocks. The second was a presentation at Enercon 2022, The 3rd International Conference on Energy and Environmental Economics, hosted by the University of the Philippines Los Banos in July. I was asked to give a presentation on the environmental Kuznets curve.

Google Scholar citations exceeded 23,000 with an h-index of 58. I wrote fewer blogposts this year. Eight in total compared to fifteen in 2021. Twitter followers rose from 1650 to almost 1750 over the year. I reviewed 13 journal articles, two tenure or promotion cases, one book proposal, and one grant proposal. I think about one of these per month is about the right number. So, I turn down quite a lot of journal article and some grant review requests. I prioritize journals that I have published in or have been reviewed by recently.

My PhD students Xueting Zhang and Debasish Das continued their research. Suryadeepto Nag has been visiting Crawford to work with me on his master's project, which I am jointly supervising, since late November. We are researching the impact of electrification on development in rural India using Indian survey data.

Looking forward to 2023, a few things can be predicted:

• I will be teaching IDEC8053 Environmental Economics in addition to Agricultural and Resource Economics in Semester 2.
• We hope to wrap up our paper on electricity markets with 100% intermittent renewables as soon as possible.
• I am also working on an ARC Discovery Projects grant proposal on electricity markets with Akshay Shanker, Tim Neal, and Michael Keane. It should be submitted at the beginning of March. 
• I'm hoping to go to my first post-pandemic in-person conference in Melbourne.

What Changed in the World Bank's Adjusted Net Saving Measure?

In August, I showed that using the World Development Indicators' current Adjusted Net Saving (ANS) data there is no relationship between ANS and the share of mining rents in GDP. I now know the main reason why this relationship appeared to change but I don't know yet why the World Bank made the changes that they did. 

In 2006 and earlier, the World Bank measured mineral and energy depletion using mining rents – the difference between mining revenues and the cost of production not including a return to the resource stock. This is based on Hartwick's Rule – resource rents should all be invested in produced capital in order to achieve sustainability. 

In recent years, they have used a different method. First, they estimate the net present value of resource rents (assuming that they remain constant in the future) using a 4% discount rate. Then they divide that amount by the number of years, T, that they assume the resource would last. The ratio of the current rent to this quantity is given by:

So, for example, if the resource has an expected 30 year lifetime then resource depletion is about 58% of current rents. Energy depletion for Saudi Arabia is around 1/3 of reported rents. This would imply that the lifetime of the resource is around 70 years.* This could explain in general why adjusted net saving is now estimated to be much higher for resource rich countries than it used to be.**

What I don't know yet is why they made this change. I haven't been able to find a rationale in the relevant World Bank publications. It is similar to but different from the El-Serafy (1989) method of measuring depletion. According to El-Serafy, the ratio of depletion to rent should be (1/(1+r))^(T+1). For a 30-year life span and a 4% discount rate, this is equal to 30%.

* The notes downloaded with the WDI data say that the lifetime is capped at 25 years. But this isn't mentioned in the relevant reports and makes the gap between rents and depletion harder to explain.

** There are a lot of other issues with assuming that the lifetime of a resource equals the expected lifetime of reserves and that rents will not change over time. There are also apparent inconsistencies between the stated methods and the results...

Do Mining Economies Save Too Little?

I'm currently teaching Agricultural and Resource Economics for the first time. This week we started covering non-renewable resources focusing on minerals. One of the topics I covered is the resource curse. One of my sources is van der Ploeg's article "Natural Resources: Curse or Blessing?" published in the Journal of Economic Literature in 2011. In the paper, he reproduces this graph from a 2006 World Bank publication that apparently uses 2003 data from the World Development Indicators:

 

Genuine saving – now known as "adjusted net saving" – is equal to saving minus capital depreciation and various forms of resource depletion with expenditure on education added on. The idea is to measure the net change in all forms of "capital" in an economy. Mineral and energy rents are the pre-tax economic profits of mining. They are supposed to represent the return to the resource stock. The graph tells a clear story: Countries whose GDP depends heavily on mining tend to have negative genuine saving. So, they are not adequately replacing their non-renewable resources with other forms of capital. Van der Ploeg states that this is one of the characteristics of the resource curse.

Preparing for an upcoming tutorial on adjusted net saving and sustainability, I downloaded WDI data for recent years for some mining intensive countries, expecting to show the students how those countries still aren't saving enough. But this wasn't the case. Most of the mining economies had positive adjusted net saving. So, I wondered whether they had improved over time and downloaded the data for all available countries for 2003:

I've added a linear regression line.* There seems to be little relationship between these variables. The correlation coefficient is -0.017. Presumably, this is because of revisions to the data since 2006.

* I dropped countries with zero mining rents from the graph. The three countries at  top right with positive adjusted net saving are Saudi Arabia, Kuwait, and Libya. Oman and then does Democratic Republic of Congo have the next highest levels of mining rents and negative adjusted net savings.

Trends in RePEc Downloads and Abstract Views

For the first time in a decade, I updated my spreadsheet on downloads and abstract views per person and per item on RePEc.

The downward trends I identified ten years ago have continued, though there was an uptick during the pandemic, which has now dissipated. There was more of an increase in abstract views than in downloads in the pandemic.

Since the end of 2011 both abstract views and downloads per paper have fallen by about 80%. Total papers rose by around 260%, while total downloads fell 38% and total abstract views 27%. 

I'd guess that a mixture of the explanatory factors I suggested last time has continued to be in play.

Confidence Intervals for Recursive Journal Impact Factors

I have a new working paper coauthored with Johannes König and Richard Tol. It's a follow up to my 2013 paper in the Journal of Economic Literature, where I computed standard errors for simple journal impact factors for all economics journals and tried to evaluate whether the differences between journals were significant.* In the new paper, we develop standard errors and confidence intervals for recursive journal impact factors, which take into account that some citations are more prestigious than others, as well as for the associated ranks of journals. We again apply these methods to the all economics journals included in the Web of Science.

Recursive impact factors include the popular Scimago Journal Rank, or SJR, and Clarivate's Article Influence score. We use Pinski and Narin's invariant method, which has been used in some rankings of economics journals. 

As simple impact factors are just the mean citations an article published in a journal in a given period receives in a later year, it is easy to compute standard errors for them using the formula for the standard error of the mean. But the vector of recursive impact factors is the positive eigenvector of a matrix and its variance does not have a simple analytical form.

So, we use bootstrapping to estimate the distribution of each impact factor. Taking all 88,928 articles published in 2014-18 in the economics journals included in the Web of Science, we resample from this dataset and compute the vector of recursive impact factors from the new dataset.** Repeating this 1,000 times we pick the 2.5% or 97.5% range of values for each journal to get a 95% confidence interval:

95% confidence intervals of the recursive impact factor, arithmetic scale (left axis) and logarithmic scale (right axis).

The graph repeats the same data twice with different scales so that it's possible to see some detail for both high- and low-ranked journals. Also, notice that while the confidence intervals for the highest ranked journals are quite symmetric, confidence intervals become increasingly asymmetric as we go down the ranks. 

The top ranked journal, the Quarterly Journal of Economics, clearly stands out above all others. The confidence intervals of all other journals overlap with those of other journals and so the ranks of these journals are somewhat uncertain.*** So, next we construct confidence intervals for the journals' ranks.

It turns out that there are a few ways to do this. We could just construct a journal ranking for each iteration of the bootstrap and then derive the distribution of ranks for each individual journal across the 1,000 iterations. Hall and Miller (2009), Xie et al. (2009), and Mogstad et al. (2020) show that this procedure may not be consistent when some of the groups (here journals) being ranked are tied or close to tied. The corrected confidence intervals are generally broader than the naive bootstrap approach.

We compute confidence intervals for ranks using the simple bootstrap, the Xie et al. method, and the Mogstad et al. method:

 

95% confidence intervals of the rank based on the recursive impact factor. The inner intervals are based on Goldstein’s bootstrap method, the middle intervals use Xie’s correction to the bootstrap, and the outer intervals follow Mogstad’s pairwise comparison.

The simple bootstrap under-estimates the true range of ranks, while it seems that the Mogstad et al. method might be overly conservative. On the other hand, Xie et al.' s approach depends on choosing a couple of "tuning parameters".  

All methods agree that the confidence interval of the rank of the Quarterly Journal of Economics only includes one. Based on the simple bootstrap, the remainder of the "Top-5'' journals are in the top 6 together with the Journal of Finance, while the Xie et al. method and the Mogstad et al. methods generally broaden estimated confidence intervals, particularly for mid-ranking journals. All methods agree that most apparent differences in journal quality are, in fact, mostly insignificant. We think that impact factors, whether simple or recursive should always be published together with confidence intervals.

* The latter exercises were a bit naive. As pointed out by Horrace and Parmeter (2017), we need to account for the issue of multiple comparisons. 

** Previous research on this topic resampled at the journal level, missing most of the variation in citation counts.

*** Overlapping confidence intervals don't neccessarily mean that there is no signficant difference between two means. Goldstein and Harvey (1995) show that the correct confidence intervals for such a test of the difference between two means are narrower than the conventional 95% confidence intervals. On the other hand, for multiple comparisons we would want wider confidence intervals.

Video of My Presentation on Asymmetric Carbon Emissions

I recently gave a seminar in Italy (virtually) on our paper on asymmetric carbon emissions over the business cycle. Here is the video:

Typo in Directed Technical Change and the British Industrial Revolution

I hate reading my papers after they're published as there is usually some mistake somewhere. Unfortunately, I have to read them to do more research. I just found a typo in our 2021 paper in JAERE. Equation (8) should look like this:

In the published paper, there is a missing Gamma in the second term. 

I also noticed a couple of issues in the text of "Energy quality" published in Ecological Economics in 2010. One is in the introduction and is debatable: "Fuel and energy quality is not neccessarily fixed". This should be or instead of and or are instead of is. But it really isn't important. Then on p1475 we have "How does these measures". Again, not important.

Of course, the error in JAERE is not very important as the third term above is correct in the published paper.

Annual Review 2021

I've been doing these annual reviews since 2011. They're mainly an exercise for me to see what I accomplished and what I didn't in the previous year. 

This was the first year since I have been back living in Canberra in 2007 that I spent the entire year in the Canberra region. In fact, it is the first year since 1991 that I didn't fly on a plane. It's not that unusual for me not to leave my country for a year. I didn't travel outside of Australia in 2019. This year, with a two year old and random snap lockdowns happening in the first part of the year, we were not in the mood to travel anywhere overnight even when it was possible. Then from mid-August came the second Canberra lockdown for 2 plus months (the first was during the first wave of the pandemic in March-May 2020). Luckily I was able to take Isaac (the two year old) to daycare throughout the lockdown, but we had to help homeschool Noah (5 years old). I was impressed how well the school organized things. Before Omicron came along, things had returned almost to normality in Canberra. We still needed to wear masks at the daycare and on the bus and needed to check in sometimes at stores etc. The university has been dragging its feet on the return to campus, but the faculty office areas in the Crawford Building have sometimes been even a little bit busy. In the last week of the year, I finally travelled out of Canberra with my family to go on holidays on the NSW South Coast.

While we've been away from Canberra the number of COVID-19 cases has been growing radically. Almost everyone here is vaccinated and Omicron seems less severe, so it's unclear what this will mean for university activity in 2022. They were planning a more or less complete return to on campus teaching, but who knows now...
 
In Semester 1 (from February to June), I again taught environmental economics and the masters research essay course. But this is the last time I will be teaching them. More about that in the 2022 predictions, below. We taught in hybrid mode. In the environmental economics class there was a joint online lecture for online and on campus students and then separate tutorials for the two groups. It turned out that very few people came to the in person tutorial. Often I had only one student. But this session was much better in my opinion than either of the online sessions. The masters research essay class had separate online and in-person classes.

 

I was awarded a Francqui Chair at the University of Hasselt in Belgium for the 2020-21 academic year. The main duty of the position was to give ten hours of lectures. Of course, I didn't actually travel to Belgium and so I gave five online lectures. You can see the videos and read some commentary on my blog.

 

I can't really think of anything notable to say about my research activity this year. It's mostly been a story of completing existing projects. We finally wrapped up our ARC DP12 project (yes, funding started in 2012 and we submitted the proposal in 2011) with the publication of our paper on the Industrial Revolution in JAERE.

I started working on several new ideas in the second half of the year but they don't seem to be going anywhere or have already been abandoned. The exception is our asymmetry paper, which we started thinking about right at the end of 2020 and now is under review.

We published five papers with a 2021 date:

Stern D. I., J. C. V. Pezzey, and Y. Lu (2021) Directed technical change and the British Industrial Revolution, Journal of the Association of Environmental and Resource Economists 8(6), 1079-1114.

Saunders H., J. Roy, I. Azevedo, D. Chakravarty, S. Dasgupta, S. de la rue du Can, A. Druckman, R. Fouquet, M. Grubb, B.-Q. Lin, R. Lowe, R. Madlener, D. McCoy, L. Mundaca, T. Oreszczyn, S. Sorrell, D. I. Stern, K. Tanaka, and T. Wei (2021) Energy efficiency: What has research delivered in the last 40 years? Annual Review of Environment and Resources 46, 135-165.

Dressel B. and D. I. Stern (2021) Research at public policy schools in the Asia-Pacific region ranked, Asia and the Pacific Policy Studies 8(1), 151-166.

Stern D. I. and R. S. J. Tol (2021) Depth and breadth relevance in citation metrics, Economic Inquiry 59(3), 961-977.

Bruns S. B., A. Moneta, and D. I. Stern (2021) Estimating the economy-wide rebound effect using empirically identified structural vector autoregressions, Energy Economics 97, 105158.

and one paper with a 2022 date:

Jafari M., D. I. Stern, and S. B. Bruns (2022) How large is the economy-wide rebound effect in middle income countries? Evidence from Iran, Ecological Economics 193, 107325.

We posted four new working papers:

How Much Does Physical Infrastructure Contribute to Economic Growth? An Empirical Analysis
December 2021. With Govinda Timilsina and Debasish Das.

Asymmetric Response of Carbon Emissions to Recessions and Expansions and Oil Market Shocks
October 2021. With Xueting Jiang.

How Large is the Economy-Wide Rebound Effect in Middle Income Countries? Evidence from Iran
August 2021. With Mahboubeh Jafari and Stephan Bruns.

Do Energy Efficiency Improvements Reduce Energy Use? Empirical Evidence on the Economy-Wide Rebound Effect in Europe and the United States
May 2021. With Anne Berner, Stephan Bruns, and Alessio Moneta.

We have three papers under review at the moment (one – the Europe rebound one – is a resubmission). There are twelve other papers on my to do list, but they range from one we are actively trying to complete, to ones that I haven't really done anything on any time recently.

Google Scholar citations exceeded 21,000 with an h-index of 55. I wrote more blogposts this year. Fifteen in total compared to ten in 2020. Twitter followers rose from 1500 to more than 1650 over the year. I did 3 external assessments of people for promotion or tenure for universities in Australia, Hong Kong, and Germany. Fewer than last year. I only did 11 reviews for journals. I used to do around double this three or more years back. And I reviewed a bunch of papers for EAERE, a proposal for the ARC, as well as giving people feedback on their papers etc.

My PhD student Xueting Zhang completed her first research year. She has made a lot of progress, with three papers at various stages of completion. My other student Debasish Das continued his work on prepaid metering and a lot of other stuff, some of which you can check out on his Google Scholar profile.

Looking forward to 2022, a few things can be predicted: 

• I will be teaching a new course (for me) in the second semester: Agricultural and Resource Economics. It is going to take a lot of work to prepare this course. 
• As a result, I won't be teaching in the first semester. Officially, I will be on long service leave, which is how I got my teaching reduced to one course for the year. But I will need to work hard on both the course and research right from the start of the year. OK, I'm feeling like taking 4th January off :) The university has encouraged us to take long service leave to help the budget situation. Taking the leave releases money from the account where it has been set aside and they don't need to pay my salary from the recurrent budget.
  
• I'm hoping we will get our paper on the rebound effect in Europe accepted very soon.
• I probably will stay in Australia for this year too. Anyway, I haven't set up any international travel at this stage.
  

Estimating the Effect of Physical Infrastructure on Economic Growth

I have a new working paper coauthored with Govinda Timilsina of the World Bank and my PhD student Debasish Das. It is a panel data study of the effect of various forms of infrastructure on the level of GDP. 

Compared to existing studies, we use more recent data, include new types of infrastructure such as mobile phones, and provide separate estimates for developing and developed countries. We find larger effects than most previous studies. We also find that infrastructure has a larger effect in more recent years (1992-2017) than in earlier years (1970-1991), and the effects of infrastructure are higher in developing economies than in industrialized economies. The long-run effects seem to be much larger than the initial impact. We also tried to estimate the effect of infrastructure on the rate of economic growth. Controlling for the initial level of GDP per worker we found a null result. So, we can't say that having more infrastructure means a more rapid rate of economic growth.

Getting good quality data that is comparable across countries is really a problem in this area of research. Many types of infrastructure only have data available for a few years. The ones that have more panel-like data often suffer from differences in definition across countries – such as what is a road or a motorway – or unexplained jumps in individual countries. So, our results are subject to a lot of measurement error.

Our main analysis uses data on five types of infrastructure – roads, railways, electric generation capacity, fixed line telephones, and mobile telephones*:

Following some previous research, we aggregate the individual types of infrastructure using principal component analysis. We use two principal components. One factor seems to be related to transport infrastructure and the other to electricity and telecommunications. Still, we can recover estimates of the effect of each individual type of infrastructure.

Also following some previous research, we use the Pooled Mean Group estimator to estimate a dynamic panel regression model. This allows us to test for the weak exogeneity of the explanatory variables, allowing us to give the results a somewhat causal interpretation.

The table shows the percentage change in GDP per worker for a 1% change in each infrastructure type. Getting standard errors for these estimates would be rather tricky.** Interestingly, the PMG estimates are mostly much larger than the static fixed effects estimates. Static fixed effects can be expected to converge to a short-run estimate of the effects while PMG should be a better estimate of long-run effects. Fixed effects also tends to inflate the effects of measurement error. 

Maybe the most innovative thing in the paper is that we plot the impulse response functions of GDP with respect to a 1% increase in each of the two main types of infrastructure:

PC1 is electricity and communications and PC2 transport infrastructure.*** Long-run effects of infrastructure are much larger than the short-run effects. In the short run, transport infrastructure even has a negative impact.

* Note that the graphs show the country means of these variables, while we actually use the deviations from those means over time in each country. 

** We only estimate the GDP-infrastructure relationship, but I think we would need time series models for each of the explanatory variables in order to sample from those models' residuals in a bootstrapping procedure. Bootstrapping is needed because we first carry out the principal components analysis and then estimate the PMG model in a second stage. These elasticities are combinations of the parameters from those two models.

*** We could get a confidence interval for these impulse response functions if we assume that the explanatory variables in the PMG model are deterministic as this analysis assumes...

The Environmental Kuznets Curve: 2021 Edition

The second encyclopedia chapter. First one is here.

Introduction 

The environmental Kuznets curve (EKC) is a hypothesized relationship between various indicators of environmental degradation and countries’ gross domestic product (GDP) per capita. In the early stages of a country’s economic development, environmental impacts and pollution increase, but beyond some level of GDP per capita (which will vary for different environmental impacts) economic growth leads to environmental improvement. This implies that environmental impacts or emissions per capita are an inverted U-shaped function of GDP per capita, whose parameters can be statistically estimated. Figure 1 shows a very early example of an EKC. A large number of studies have estimated such curves for a wide variety of environmental impacts ranging from threatened species to nitrogen fertilizers, though atmospheric pollutants such as sulfur dioxide and carbon dioxide have been investigated most. Panayotou (1993) was the first to call this relationship the EKC, where Kuznets refers to the similar relationship between income inequality and economic development proposed by Nobel Laureate Simon Kuznets and known as the Kuznets curve. The EKC can be seen as an empirical version of the interpretation of sustainable development as the idea that development is not necessarily damaging to the environment and, also that poverty reduction is essential to protect the environment (World Commission on Environment and Development, 1987).

Figure 1. An Environmental Kuznets Curve 

The EKC has been the dominant approach among economists to modeling ambient pollution concentrations and aggregate emissions since Grossman and Krueger (1991) introduced it in an analysis of the potential environmental effects of the North American Free Trade Agreement. The EKC also featured prominently in the 1992 World Development Report published by the World Bank and has since become very popular in policy and academic circles and is even found in introductory economics textbooks. 

Critique 

Despite this, the EKC was criticized almost from its beginning on empirical and policy grounds, and debate continues. It is undoubtedly true that some dimensions of environmental quality have improved in developed countries at the same time that they have become richer. City air and rivers in these countries have become cleaner since the mid-20th Century and, in some countries, forests have expanded. Emissions of some pollutants such as sulfur dioxide have clearly declined in most developed countries in recent decades. But there is more mixed evidence for other pollutants such as carbon dioxide. Carbon emissions have fallen in the last 40 years in some developed countries such as the United Kingdom or Sweden, while they have increased in others such as Australia or Japan. There is also evidence that emerging countries take action to reduce severe pollution. For example, Japan cut sulfur dioxide emissions in the early 1970s following a rapid increase in pollution when its income was still below that of the developed countries (Stern, 2005) and China has also acted to reduce sulfur emissions in recent years. As further studies were conducted and better data accumulated, many of the econometric studies that supported the EKC were found to be statistically fragile. 

Initially, many understood the EKC to imply that the best way for developing countries to improve their environment was to get rich (e.g. Beckermann, 1992). This alarmed others (e.g. Arrow et al., 1995), as while this might address some issues like deforestation or local air pollution, it would likely exacerbate other environmental problems such as climate change. Even if there is an EKC for per capita impacts, environmental impacts would increase for a very long time if the majority of the population is on the rising part of the curve and/or the population is also growing (Stern et al., 1996). 

Explanations 

The existence of an EKC can be explained either in terms of deep determinants such as technology and preferences or in terms of scale, composition, and technique effects, also known as “proximate factors”. Scale refers to the effect of an increase in the size of the economy, holding the other effects constant, and should increase environmental impacts. The composition and technique effects must outweigh this scale effect for pollution or other environmental impacts to fall in a growing economy. The composition effect refers to the economy’s mix of different industries and products, which differ in pollution intensities. Finally, the technique effect refers to the remaining change in pollution intensity. This will include contributions from changes in the input mix, for example substituting natural gas for coal; changes in productivity that result in less use, ceteris paribus, of polluting inputs per unit of output; and pollution control technologies that result in less pollutant being emitted per unit of polluting input. 

Over the course of economic development, the mix of energy sources and economic outputs tends to evolve in predictable ways. Economies start out mostly agricultural and the share of industry in economic activity first rises and then falls as the share of agriculture declines and the share of services increases. We might expect the impacts associated with agriculture, such as deforestation, to decline, and naively expect the impacts associated with industry, such as pollution, would first rise and then fall. However, the absolute size of industry rarely does decline, and it is improvement in productivity in industry, a shift to cleaner energy sources, such as natural gas and hydro-electricity, and pollution control that eventually reduce some industrial emissions. On the other hand, offshoring of pollution probably plays only a small role in cutting emissions in developed economies (Kander et al., 2015). 

Static theoretical economic models of deep determinants, that do not try to also model the economic growth process, can be summarized in terms of two parameters: The elasticity of substitution between dirty and clean inputs, which summarizes how difficult it is to cut pollution; and the elasticity of the marginal utility of consumption with respect to consumption, which summarizes how hard it is to increase consumer well-being with more consumption (Pasten and Figeroa, 2012). It is usually assumed that these consumer preferences are translated into policy action. Pollution is then more likely to increase as the economy expands, the harder it is to substitute other inputs for polluting ones and the easier it is to increase consumer well-being with more consumption. If these parameters are constant, then either pollution rises or falls with economic growth. Only if they change over time will pollution first rise and then fall. The various theoretical models can be classified as ones where the EKC is driven by changes in the elasticity of substitution as the economy grows or models where the EKC is primarily driven by changes in the elasticity of marginal utility. 

Dynamic models that model the economic growth process alongside changes in pollution are harder to classify. The Green Solow Model developed by Brock and Taylor (2010) explains changes in pollution as a result of the competing effects of economic growth and a constant rate of improvement in pollution control. Fast growing middle-income countries, such as China, then having rising pollution, and slower growing developed economies, falling pollution. An alternative model developed by Ordás Criado et al. (2011) also suggests that pollution rises faster in faster growing economies but that there is also convergence so that countries with higher levels of pollution are more likely to reduce pollution faster than countries with low levels of pollution. 

Recent Empirical Research 

Recent empirical research builds on these dynamic models to paint a subtler picture than early EKC studies did (Stern, 2017). We can distinguish between the impact of economic growth on the environment and the effect of the level of GDP per capita, irrespective of whether an economy is growing or not, on reducing environmental impacts. We can also distinguish between the effects of economic growth and the simple passage of time. Economic growth usually increases environmental impacts, but the size of this effect varies across impacts and the impact of growth often declines as countries get richer. However, richer countries are often likely to make more rapid progress in reducing environmental impacts. In econometric terms, the time effect – the change in emissions if economic growth is zero – may be higher in richer countries. Rapid growth in middle-income countries, such as China or India, is more likely to overwhelm the time effect in those countries as suggested by Brock and Taylor (2010).

Finally, there is often convergence among countries, so that those that have relatively high levels of impacts reduce them faster or increase them slower than countries with low levels of impacts. These combined effects explain more of the variation in pollution emissions or concentrations than either the classic EKC model or models that assume that either only convergence or growth effects alone are important. Therefore, while being rich means a country might do more to clean up its environment, getting rich is likely to be environmentally damaging. 

References 

Arrow, K., Bolin, B., Costanza, R., Dasgupta, P., Folke, C., Holling, C. S., Jansson, B.-O., Levin, S., Mailer, K.-G., Perrings, C., Pimental, D., 1995. Economic growth, carrying capacity, and the environment. Science 268, 520–521. 

Beckermann, W., 1992. Economic growth and the environment: whose growth? Whose environment? World Development 20, 481–496. 

Brock, W. A.,Taylor, M. S., 2010. The green Solow model. Journal of Economic Growth 15, 127–153. 

Grossman, G. M., Krueger, A. B., 1991. Environmental impacts of a North American Free Trade Agreement. NBER Working Papers 3914. 

Kander, A., Jiborn, M., Moran, D. D., Wiedmann T. O., 2015. National greenhouse-gas accounting for effective climate policy on international trade. Nature Climate Change 5, 431–435. 

Ordás Criado, C., Valente, S., Stengos, T., 2011. Growth and pollution convergence: Theory and evidence. Journal of Environmental Economics and Management 62, 199–214. 

Panayotou, T., 1993. Empirical tests and policy analysis of environmental degradation at different stages of economic development. Working Paper, Technology and Employment Programme, International Labour Office, Geneva, WP238. 

Pasten, R., Figueroa, E., 2012. The environmental Kuznets curve: A survey of the theoretical literature. International Review of Environmental and Resource Economics 6, 195–224. 

Stern, D. I., 2005. Beyond the environmental Kuznets curve: Diffusion of sulfur-emissions-abating technology. Journal of Environment and Development 14(1), 101–124. 

Stern, D. I., 2017. The environmental Kuznets curve after 25 years. Journal of Bioeconomics 19, 7–28.

Stern, D. I., Common, M. S., Barbier, E. B., 1996. Economic growth and environmental degradation: the environmental Kuznets curve and sustainable development. World Development 24, 1151–1160. 

World Commission on Environment and Development, 1987. Our Common Future. Oxford: Oxford University Press.