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.