First up, why? Sports economics is a popular course. Students are enthusiastic about the topic, and there are many intriguing economic features: game theory, of course, but also discrimination, contracts, financial markets, technological progress … Not all students like sports. Some like scifi & fantasy. A lecture would start with a quote from and a sketch of a classic book or movie — and use this as a wedge into a part of economics.

But where to start? I posted the opening on social media. Faculty are enthused.

George R.R. Martin asked “What was Aragorn’s tax policy?” We do not know. The Lord of the Rings only sketches what happened after he was crowned King Ellesar of Gondor and Arnor. Tolkien was anyway not much concerned with economics. Sauron’s armies were fed from large slave plantations in the south of Mordor. Isengard imported pipeweed from the Shire. Most of his characters are upper class, unconcerned with making a living. Many read his work as a battle between an agrarian society and an industrial one.

There are at least two papers on the economics of Middle Earth:

Kelly, Steven (15 April 2016). “Breaking the Dragon's Gaze: Commodity Fetishism in Tolkien's Middle-earth”. Mythlore. 34 (2). Article 8. ISSN 0146-9339.

Lacon, Ruth (1994). “The economy and economic history of Gondor”. Mallorn https://journals.tolkiensociety.org/mallorn/article/download/13/9/19

It even has a lemma on Wikipedia, including a discussion on the monetary impact of Smaug’s gold hoard — but not its sudden release. Duncan Weldon does write about the latter in his 2025 book Blood and Treasure.

Paul Krugman wrote a classic on the economics of interstellar trade, including the impact of time dilation on interest rates. Science fiction writers tend to have a weaker grasp of economics. In Dune, for instance, Frank Herbert has Caladan exporting wine and rice. At the moment, it costs about $2500 to lift a kilogram into orbit. Grapes will not grow on Mars: Too cold, too dark, wrong soil. Elon Musk will be able to afford earth wine, but most Martians will not — poo-grown potato vodka for them. Slower-than-light interstellar trade plays a key role in Charles Stross’ Neptune’s Brood.

The spice market is fascinating: A dominant supplier (whoever controls Arrakis) plus a fringe (smugglers), a dominant buyer (CHOAM) with another fringe (nobility), stockpiles, and an inferior alternative (melange). Poor students: Come for space adventure and get industrial organization.

Psychohistory is a key part of Asimov’s Foundation and an excellent starting point for a discussion of economic forecasting. Sci-fi, of course, frequently depicts dystopian or utopian futures — The Rediscovery of Man and The Expanse are exceptions, muddling through in an utterly changed world.

Other suggestions include:

• Futurama / Down and Out in the Magic Kingdom, about a post-scarcity society

• Firefly / Mad Max, about the fight for resources

• John Brunner’s Stand on Zanzibar, about overpopulation

• Ursula le Guin’s The Dispossessed / Robert Heinlein’s The Moon is a Harsh Mistress / Ada Palmers’s Too Like The Lightning, about alternative ways to organize society

• Cities in Flight, about currency, energy, mobility, sustainability

• Altered Carbon, about eternal life

• The Dark Forest / The Forbin Project, about game theory

• DiscWorld, about poverty, mail, making money

• The Ministry of the Future (I did not like his early books and then I met KSR and liked him even less)

• Markets in videogames

• Supervillains and organized crime

Books on the topic include:

• Manu Saadia (2016), Trekonomics, Pipertext.

• William Davies (ed.) (2018), Economic Science Fictions, Goldsmiths Press.

• Dave Schultz et al. (eds.) (2018), Strange Economics, tdotSpec.

• Richard Davies (2020), Extreme Economics, Black Swan. (This is actually about the economies of people in extreme but real situations.)

• Francois Bourguignon et al. (eds.) (2025), Economics and Literature, Routledge.

Christine Neill of Wilfried Laurer U taught a similar course twice and kindly shared her syllabi. Part of the assessment was a project: Groups of students read a novel and wrote a report on the economics in that book.

Much too ponder. More later.

The paper makes two simple points. Stabilization of carbon dioxide emissions (a first step to halting anthropogenic climate change) between 2010 and 2060 requires an emission reduction (from what was then a reasonable scenario) of some 7 billion tonnes of CO2. It identifies 15 then-proven technologies that could each be scaled up to avoid 1 billion tonnes.

That’s it. There is no assessment of costs and benefits, let alone trade-offs or portfolio selection. There is no discussion of policies to get from here to there. Just back-of-the-envelope calculations. People who had published more sophisticated work in less prestigious journals began referring to “wedgies”.

The journalists make much of Pacala and Socolow underestimating the prospects of renewables and overestimating those of carbon capture and storage. Stone detects the malign influence of BP in this, overlooking that this was the majority view at the time — and that BP was investing in both technologies.

George Monbiot thinks that Pacala and Socolow did much to block climate policy. While Maddie Stone is too young to recall the climate debate around 2004, Monbiot is not. The wedgies, in fact, reassured many people that emission reduction is feasible — as Andy Revkin reminds us.

Monbiot and Revkin both wonder about corporate funding of research. I think that an engineering school should collaborate with companies that are prospective employers of its students. Technocratic companies like BP and Ford have an interest in objective research, at least in private. There is nothing suspicious here, and anyone who knows Steve and Rob knows they cannot be bought.

Pielke Jr has the opposite take. Where Monbiot sees a pillar of climate denial exposed, Pielke sees a pillar of climate advocacy collapsed. He even calls for the paper to be retracted, claiming it was ghost-written.

Pielke revisits an old debate: Do we have the technology to solve the climate problem or do we need more research? I never understood this. Technical solutions to reduce anthropogenic carbon dioxide emissions to zero have long existed. More research is needed to make these technologies more attractive. That was true in 2004 and it is true today.

Disclosure: I was to spend a sabbatical at Princeton with David Bradford. I ended up spending time with Steve Pacala and Rob Socolow.

The geographic distribution of sexual minorities reflects patterns of urbanisation, social tolerance, and economic opportunity. We combine gridded mean annual surface air temperature from the UK Met Office HadUK-Grid 30-year climatology (1991–2020) with Local Authority District (LAD)-level sexual orientation counts from the 2021 England and Wales Census to test whether lesbian, gay, and bisexual (LGB) individuals inhabit systematically warmer locations than their heterosexual peers. Across 311 matched LADs, the population-weighted mean annual temperature is 10.45°C for LGB residents compared with 10.33°C for straight/heterosexual residents—a difference of +0.12°C. This thermal differential reflects the well-documented concentration of LGB populations in major urban centres, particularly London, rather than any direct causal mechanism. Because the gridded temperature product does not resolve the urban heat island effect at its 5 km spatial resolution, our estimate constitutes a lower bound on the true population-weighted temperature differential.

Introduction

Sexual minorities are not uniformly distributed across geographic space. In the United Kingdom, as in comparable economies, LGB individuals are disproportionately concentrated in large cities, drawn by economic opportunity, social anonymity, and the density of community infrastructure (1, 2). The geography of climate across the UK is similarly structured: mean annual temperatures decrease with latitude and elevation, and increase with proximity to the Atlantic and to urban centres (3). Whether these two spatial gradients—of sexual minority concentration and of temperature—overlap sufficiently to produce a detectable aggregate thermal signature has not previously been examined. Here we quantify this effect using publicly available census and climate data at LAD resolution for England and Wales.

Results

Temperature assignment. Mean annual surface air temperature from the HadUK-Grid 5 km climatology was aggregated to 361 UK LAD boundaries by nearest-centroid (Voronoi) tessellation, assigning each 5 km grid cell to the LAD whose published centroid lay nearest in British National Grid coordinates. LAD mean temperatures ranged from 3.4°C (Highland, Scotland) to 12.1°C (several inner London boroughs), consistent with established north–south and urban–rural gradients (3).

Sexual orientation. Census table TS077 records sexual orientation for 43.1 million England and Wales residents aged 16 and over across 331 LADs. Of these, 41.7 million (96.6%) identified as straight/heterosexual, 1.46 million (3.4%) as LGB (gay or lesbian, bisexual, or another specific orientation), and 2.9 million did not answer. The LGB fraction varies substantially across LADs, from 1.7% in several rural districts to 11.7% in the City of London. High-LGB LADs cluster in inner London, Brighton and Hove, and central Manchester.

LGB individuals live in warmer places. Restricting analysis to the 311 England and Wales LADs with matched temperature estimates, the population-weighted mean annual temperature is 10.45°C for LGB residents (n = 1,462,901) and 10.33°C for straight/heterosexual residents (n = 41,656,347)—a difference of +0.12°C (Table 1). Residents who did not answer the sexual orientation question were excluded from both groups.

Table 1. Population-weighted mean annual temperature by sexual orientation group, England and Wales.
Straight/Heterosexual: 10.33°C (n = 41,656,347)
LGB: 10.45°C (n = 1,462,901)
Difference (LGB − Straight): +0.12°C

Discussion

The +0.12°C thermal differential between LGB and straight populations in England and Wales is consistent with the spatial concentration of sexual minorities in large cities. London is the dominant driver: it accounts for a disproportionate share of the UK LGB population, and its mean annual temperature exceeds the national average by virtue of both its southerly latitude (~51.5°N) and its urban character. Brighton and Hove, the only LAD outside London with an LGB fraction above 8%, is similarly coastal and mild.

A critical caveat concerns the temperature data. HadUK-Grid is produced by interpolating meteorological station observations—many deliberately sited to minimise urban exposure—onto a 5 km regular grid (3). At this resolution, the urban heat island (UHI) effect is largely absent from the gridded product. The UHI in central London is estimated at approximately 4°C relative to the rural surroundings (4), yet this signal does not appear in HadUK-Grid. Because LGB individuals are concentrated in exactly those dense urban environments where the UHI is strongest, our estimate of +0.12°C almost certainly understates the true population-weighted temperature differential. Work is ongoing to obtain the MODIS land surface temperature climatology at 1 km resolution from the Integrated Climate Data Center, University of Hamburg (5), which measures radiant surface temperature directly from satellite and resolves intra-urban thermal gradients.

Two further limitations apply. First, the sexual orientation question in the 2021 Census was not administered in Scotland or Northern Ireland, precluding UK-wide analysis. Second, 20 England and Wales LADs in the census file could not be matched to temperature estimates, likely owing to boundary differences between the 2021 census geography and the May 2024 LAD shapefile; their exclusion is unlikely to bias the result materially.

Materials and Methods

Temperature data. Mean annual surface air temperature was obtained from the HadUK-Grid 5 km resolution 30-year climatology for 1991–2020 (Met Office Hadley Centre, 3). The NetCDF4/HDF5 binary was parsed without third-party geospatial libraries. The zlib-compressed, HDF5-shuffle-filtered data block was located at byte offset 13,689. The inverse shuffle transform for 8-byte (float64) elements was applied prior to decompression. Grid coordinates in British National Grid (OSGB36/EPSG:27700) were recovered from byte offsets 95,167 (180 easting values) and 105,933 (290 northing values), at 5,000 m spacing. Values outside −20 to +25°C were masked.

LAD boundaries and temperature aggregation. Local Authority District boundaries for May 2024 were obtained from the ONS Open Geography Portal. Centroid coordinates were extracted from the accompanying DBF attribute table. Each 5 km grid cell was assigned to the nearest LAD centroid (Voronoi tessellation). LAD mean temperatures were computed as the arithmetic mean of all valid assigned grid cells.

Sexual orientation data. Census table TS077 was obtained from the Office for National Statistics. Respondents classified as gay or lesbian, bisexual, or "all other sexual orientations" were pooled into a single LGB group. Those not answering were excluded from both the LGB fraction and the population-weighted temperature calculation.

Population-weighted temperatures. For each group g ∈ {straight, LGB}, the population-weighted mean temperature was computed as T̄g = Σi Ti nig / Σi nig, where Ti is the mean temperature of LAD i and nig is the population of group g in LAD i, summed over the 311 England and Wales LADs with valid temperature estimates. All computations were performed in Python using NumPy; visualisations were produced with Matplotlib.

References

1. G. Gates, L. Frisch, Where the Gays Are: The Social and Economic Geography of Gay and Lesbian America (Urban Institute Press, 2003).

2. G. Brown, Scalar politics, the metropolitan glance and the construction of gender and sexuality in British cities. Environ. Plan. A 40, 942–960 (2008).

3. M. R. Hollis et al., HadUK-Grid—A new UK dataset of gridded climate observations. Geosci. Data J. 6, 151–159 (2019).

4. C. S. B. Grimmond, T. R. Oke, Heat storage in urban areas. J. Appl. Meteorol. 38, 922–940 (1999).

5. Integrated Climate Data Center, University of Hamburg, MODIS Land Surface Temperature Climatology (2024). https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-lst-annualcycle.html

PS This is my fourth attempt at vibe-coding. Claude is an energetic and skilled research assistant, but rushes in without planning and has poor critical facilities. The first three attempts were failures: Replicating a paper with a non-standard estimator failed, as did attempts to create polygons of countries between 1946 and 2025 and the Roman Empire in 177CE. This was a simpler task. Use existing polygons to compute area temperatures from a grid; compute a weighted average for the two countries; write it up as Science paper; and publish on Substack.

A wise man once said that 100 papers is a career. He died with a smile on his face, doing one of the things he liked best. Perhaps I should have listened. I have published 318 papers. You can publish lots if you have a large team of sound assistants and students or a paper-generating mechanism, a model or a data set so rich that it keeps giving. For a while, I had both. There is a danger here: You will lose touch if, year after year, you outsource all data-cleaning and coding. Another variant on the same gets boring.

Preferences matter. I think programming is more fun than editing an assistant’s prose. So, partly by happenstance and partly by design, I am again doing most of my own research: I clean the data, I programme the code, I write the paper. Much like that wise man. I do not play polo.

I still have rich, partly unexplored datasets that no one else has, and a model that can be reconfigured and reconfigured again. Some former assistants and students have become collaborators. New students arrive. New alliances are formed. So, I still publish lots. IDEAS/RePEc puts me ninth. Peter Nijkamp set the tone where I was young.

So, who am I to talk about the quantity v quality trade-off? I recoil when I see that someone published 53 papers in 2025; or hear that someone else signed a contract to publish 100 papers per year. Abramo and d’Angelo (2025) put the threshold for hyperprolificity (in economics and finance) at 1 paper per month. I crossed that threshold more than once.

I understand how you can do a paper or two per week. Recycle parts of papers. Apply the same method to multiple datasets, or multiple methods to the same data. Co-authorship by broad guidance and superficial edits is the fastest way to increase your output. I am reminded of this guy who lost his job because one of his papers contained a major diplomatic faux-pas — which can only mean he had not carefully read any of the draft versions. You should not write faster than you can read.

Students are not helped if you just put your name on their paper. You help them by discussing every stage of their research. You cannot help if you let your own research skills atrophy. This limits the number of students you can advise and so the number of co-authored papers.

Ditto for collaborations. You are a co-author if the paper would have been materially different without your input. Can you do that one or twice a week? No, you cannot. You can lend your name to a paper to boost its chance of publication — but that only works with journals you should not want to publish in.

If you worked with someone before, collaboration can be smooth and efficient. You know their weak spots and so which parts of the paper need to be checked twice. New co-authors take more time. Networks of proper collaboration grow slowly and organically. Networks of superficial collaboration can grow fast.

Current research into hyperactive authors focus on output. I would add two indicators: solo-authored papers, and network formation.

What’s the way forward? Adrian Barnett recommends we all slow down. This is a bit silly. We’re in a rat race. If you slow down, you hurt your career and the careers of your students and post-docs. The academy is too large, too anonymous for Ostrom-like self-regulation. We’re in a bad equilibrium. We need an external shock to get us out.

I am no fan of the UK Research Excellence Framework (REF). It counts your best two papers of the last six years. This rewards slow science and promotes excellence. However, some of the people here are lazy rather than slow. More importantly, the REF punishes sound, applied research. We expect PhD candidates to write three papers in three years. Their professors can get away with just one?

Anyway, the UK is too small to change the world. A substantial part of the problem originates in younger, less well-established national academies, where inexperienced regulators cannot recognize quality and so opt to reward quantity instead. The Chinese authorities worry about involution, excessive competition between manufacturers. Perhaps they should also be concerned about academic involution. They should first do away with cash bonuses for publications and then follow the REF and gradually raise the bar — from one paper per week to one per month to one per year.

I used to be a silly person.*

Elsevier pressured me to increase the number of published papers. I had assumed that this was about market share. The number of scholars is increasing, particularly in poorer countries where energy is relatively more important. Instead, it was about profits. The switch to open-access is not as innocent as I once thought. We used to pay to read. Publishers exercised monopoly power,** which increased in the prestige and quality of the journal. Many universities had a package deal, so the marginal cost of reading a paper was zero. We now pay to publish. Publishers exercise monopsony power,** which increases in the prestige and quality of the journal. Many universities have a package deal, but with a cap on the number of papers that can be published for free. Other universities do not have a package deal. Profits thus increase with volume. Publishers trade off higher returns in the short run with maintaining prestige in the long run.

Open access has failed. Papers are accessible in principle but just as unreadable to the uninitiated as they have always been. Incentives changed at the margin. Quantity is up, quality is down.

The number of submissions to Energy Economics grew faster than the number of editors. We solved this with special issues and guest editors. I see editing a journal as a service to the community; guest editors as selfless servants. Editorial work is a criterion for promotion at some universities, but such guest editors would still want to do a good job. Some people see opportunities for patronage, but we kept that in check with teams of guest editors and random assignments within such teams. That model was not robust to token editors and people publishing their girlfriend’s papers.

Journal articles have long been important for promotion and thus have economic value for authors. Some universities give teaching relief to those who publish well. An increasing number of universities in East and West Asia pay bonuses for publications.*** This is a game-changer. Fake authors is one thing. In the end, it does not matter whether a good piece of research has one or five authors — although four may have defrauded their employer.**** But, if five authors get a $5,000 bonus, they can easily spare $1,000 each to bribe a (guest) editor. That quickly adds up.

Should I have spotted these three changes — open access, patronage, bribery? I did find out, but late, and we retracted more papers than is healthy (but fewer papers than we should have.) In my defense, editing a large journal is a lot of work, on top of being a researcher, a professor, a husband, and a father. Elsevier never briefed us on any of this — indeed, I alerted them.

I am wiser now, only to discover that Elsevier does not look kindly on whistleblowers. I hope that people reading this benefit from the knowledge gained at my expense.

* This is a quote by Bill Nordhaus. He spoke these words at a large OECD/IEA conference to the horror of the non-academics in the audience. Bill referred to the double dividend, the interaction between internalisation of environmental externalities and fiscal distortions.

** Publishers are natural monopolies as it takes decades to build a highly reputed journal. Natural monopolies should be strictly regulated. One option is to charge royalties — publishers profit, after all, from the research base funded by public money. For instance, publishers could be made to donate all profits in excess of a 10% margin to the national science foundations.

*** I understand that someone just signed a contract for a secondary affiliation, stipulating that he publish 100 papers per year. (Yes, really.)

**** Some employers may not be equipped to deal with this. Or they may not care. I have reported the sale of authorships to several universities, but only a minority responds. One head of department attacked me. I also encountered a junior scholar who was astonished that I would not accept a bribe; his professors had told him this was the only route to publication.

The model updates a previous one. Conditional on being Nobel-worthy (as signified by the John Bates Clark Medal, the Yrjo Jahnsson Award, or the Clarivate Citation Laureates), significant predictors include the total number of citations, the proximity to the Nobel co-author network, and the proximity to Nobel family (both actual and academic).

My favourites, Bhagwati and Krueger, are near the bottom.

1. Robert J. Barro 36.23%

2. Carmen M. Reinhart 28.70%

3. Andrei Shleifer 18.07%

4. Richard G. Lipsey 13.99%

5. Ernst Fehr 6.33%

6. Bronwyn H. Hall 4.50%

7. Helene Rey 3.81%

8. Robert W. Vishny 3.61%

9. Kenneth R. French 3.49%

10. George F. Loewenstein 2.98%

11. Ricardo J. Caballero 2.79%

12. Lawrence H. Summers 2.65%

13. John Y. Campbell 2.44%

14. Melissa Dell 2.38%

15. Michael C. Jensen 2.27%

16. Samuel Bowles 2.13%

17. Kenneth S. Rogoff 2.10%

18. Matthew J. Rabin 1.98%

19. Kalina Manova 1.93%

20. Paolo Mauro 1.43%

21. M. Hashem Pesaran 1.35%

22. Guido Tabellini 1.33%

23. Janet M. Currie 1.29%

24. Olivier J. Blanchard 1.29%

25. Jerry A. Hausman 1.23%

26. Mark L. Gertler 1.21%

27. Marc L. Nerlove 1.21%

28. Jordi Gali 1.21%

29. Robert E. Hall 1.17%

30. Peter W. Howitt 1.16%

31. David J. Teece 1.13%

32. Edward L. Glaeser 1.09%

33. Stephen R. Bond 0.99%

34. Steven D. Levitt 0.99%

35. W. Brian Arthur 0.96%

36. Peter C.B. Phillips 0.95%

37. Ariel Pakes 0.88%

38. Daniel A. Levinthal 0.87%

39. Charles F. Manski 0.86%

40. Nicholas Bloom 0.86%

41. Richard W. Blundell 0.85%

42. John M. van Reenen 0.84%

43. Tim Bollerslev 0.84%

44. Gene M. Grossman 0.83%

45. Pierre Perron 0.83%

46. Sam B. Peltzman 0.79%

47. Kevin M. Murphy 0.77%

48. Matthew A. Gentzkow 0.77%

49. Fabrizio Zilibotti 0.75%

50. Partha Dasgupta 0.75%

51. Nobuhiro Kiyotaki 0.74%

52. Ricardo A.M.R. Reis 0.74%

53. Marc J. Melitz 0.74%

54. Philippe M. Aghion 0.73%

55. John A. List 0.73%

56. Amy N. Finkelstein 0.73%

57. Colin F. Camerer 0.71%

58. Manuel Arellano 0.68%

59. Susan C. Athey 0.68%

60. Elhanan Helpman 0.68%

61. Raj Chetty 0.66%

62. Sanford J. Grossman 0.66%

63. Emmanuel Saez 0.64%

64. Raghuram G. Rajan 0.62%

65. Gilles Saint-Paul 0.62%

66. Jan K. de Loecker 0.61%

67. Wesley M. Cohen 0.60%

68. John H.H. Moore 0.59%

69. Avinash K. Dixit 0.59%

70. Emi Nakamura 0.56%

71. Timothy J. Besley 0.56%

72. Roland G. Fryer 0.54%

73. David M. Kreps 0.53%

74. Stewart C. Myers 0.51%

75. Torsten Persson 0.51%

76. David B. Audretsch 0.50%

77. Thomas Piketty 0.49%

78. Richard A. Posner 0.48%

79. Andrew J. Oswald 0.46%

80. Soren Johansen 0.44%

81. Mark S. Granovetter 0.42%

82. Parag A. Pathak 0.42%

83. Israel M. Kirzner 0.42%

84. Guillermo A. Calvo 0.41%

85. Silvana Tenreyro 0.41%

86. Mathias F. Dewatripont 0.39%

87. Jonathan Levin 0.39%

88. John B. Taylor 0.38%

89. Dave Donaldson 0.36%

90. Armin Falk 0.36%

91. James A. Levinsohn 0.36%

92. Imran Rasul 0.35%

93. Oriana Bandiera 0.35%

94. Evan L. Porteus 0.34%

95. David F. Hendry 0.34%

96. Botond Koszegi 0.34%

97. Francine D. Blau 0.33%

98. Oleg Itskhoki 0.33%

99. Richard Layard 0.33%

100. Katarina Juselius 0.30%

101. Michele Tertilt 0.28%

102. Wayne A. Fuller 0.26%

103. David A. Dickey 0.25%

104. Ran Spiegler 0.23%

105. Jagdish N. Bhagwati 0.21%

106. Hans-Werner Sinn 0.19%

107. Ariel Rubinstein 0.17%

108. Yuliy Sannikov 0.17%

109. Richard A. Easterlin 0.16%

110. Anne O. Krueger 0.14%

111. Deirdre N. McCloskey 0.14%

112. Joel Mokyr 0.13%



On 17 July 2025, Elsevier fired me with immediate effect. I had led Energy Economics from a struggling second-tier field journal to the journal with the highest impact factor of all economics and finance journals, a journal that has a more diverse authorship than any other journal in economics, the journal that publishes more papers than most economics journals and brings in more revenue than all but one — all on a shoestring budget.

The decision was pointless. I had decided to step down before Christmas 2025 and had informed the publisher of my decision. This would have allowed me to conclude open investigations into fraud, see the papers in my care to the end, and gradually hand over to my successor.

My abrupt termination threw the journal into chaos. You would think that they had found kiddy porn on my computer.

But no. Elsevier gave two reasons for my dismissal. First, they do not like it when an academic, in charge of an academic journal, talks to other academics about an academic journal as an academic would. A senior official at Elsevier took umbrage when I told an author that he had faked data.

Second, Elsevier did not accept our collective decision to raise the quality standard for finance papers to the same level as for economics papers. This is an editorial decision.

I think the real reason is different. I recently had dinner with some editors of Elsevier’s finance journals. This was not a meeting of competing journals. It was a meeting of a group of friends who had cornered a market.

Some of the editors at the dinner table have been credibly accused of improper citation, manipulating peer review, soliciting financial and sexual favours, and accepting financial bribes.*

Elsevier have pressured Energy Economics to increase the volume of published papers. The publishing director said she values quantity over quality.

Under pressure, we increased the number of special issues — until we discovered that some of our guest editors could not be trusted. One case, with dubious reviews and allegations of money changing hands, was reported to Elsevier in August 2023. No action was taken on the credible evidence of bribery. The publisher ignored my retraction order for fake peer review.

There were other problems at Energy Economics. We had a budget to audit only a fraction of replication packages. Post-publication audits revealed many problems due to a mix of clumsiness and fraud. Some authors were manipulating citations, and some guest editors were too close to the authors in their charge. In the dozens of investigations of the last two years, some names kept popping up — many of them editors of other Elsevier journals and some at the dinner table above. I repeatedly reported this to the publisher, but with no discernible impact.**

It seems to me, therefore, that the real reason I was fired is that I got too close to exposing serious fraud at Elsevier.

There have been no retractions from Energy Economics over the summer — even retractions agreed to by the authors have yet to materialize. Too many retractions would have been a good reason to fire me. Indeed, publishing too many papers that should later be retracted is one of the reasons I decided to step down before my contract ended — but after I had cleaned up the mess. Elsevier seem to think it is better not to retract bad papers.

*Some have been accused of much worse.

**One editor was allegedly removed for an unrelated infraction: editing his own paper.

Retraction would be too harsh.* Kopp seems just as upset about the bad publicity as about the bad science. So, why did I sign? First of all, I expect the editors to invite us to write a comment instead. Second, strictly, the Voortman & de Vos were dishonest: They claim to share their code, but they do not. Third, the statistical analysis is really terrible.

Retracting a paper for incompetent statistics is a slippery slope, one I am prepared to glide down. The standard of statistical analysis in the geosciences is typically abysmal. I have yet to meet a geoscientist who knows Haavelmo (1944). More down to Earth, Yule (1927) and Engle & Granger (1987) appear to have passed them by.

In the case of Voortman & de Vos, the applicable test for a trend break is Zivot & Andrews (1992). Instead, they correct for autocorrelation following Bence (1992), apparently oblivious to the bias due to higher-order autocorrelation and the singularity due to a unit root.

So, part of me hopes against hope that calling out Voortman & de Vos will lead to some reflection on statistical standards in the geosciences. For instance, the recent paper by Wang et al. (2025) would not pass as an undergraduate thesis in econometrics: The estimator is inefficient, unnecessarily so, and key tests are missing. It is not enough to test a linear trend against no trend. You need to test trend stationarity against non-stationarity. Wang study sea level acceleration and should have tested their non-linear trend against second-order integration and against trend stationarity with one or more breaks. They did not do this so their results are inconclusive.

*UPDATE: The special issue editors have disowned the paper.

Baurkamp, Hogan and Hsiang note that the results are sensitive to the — probably wrong — data for Uzbekistan. Schoetz notes that the results are not robust to spatial autocorrelation.

This is serious, but you have to wonder why this paper was published. It does not add much to an earlier paper by the same authors, which did not add much to an earlier paper by the same authors and two others, which did not match to an earlier paper by two of these authors, which was a not very original variant of an earlier paper, itself an inferior response to the original paper by Melissa & the Bens. How did such a derivative paper get into Nature?

The Network for the Greening of the Financial Sector, a club of central banks that lost momentum after the elections of Carney and Trump, adopted the paper. Why? I did pose this question, in private, to a few central bankers, adding that they have great in-house expertise in econometrics. I did not get an answer beyond an embarrassed look.

The problems with KLW go much further than identified by Baurkamp and Schoetz. The paper reads like an exercise in data mining. There are just too many explanatory variables: Temperature, temperature variability, rainfall, number of wet days, and rainfall extremes, each differenced and interacted with their level, with lags to 10 years. 110 weather variables in total! They imposed no structure and got some fairly implausible results. For instance, the change in the annual temperature has no effect for the first ten years, but then it hits hard. 2024 was some 0.15K warmer than 2023. If KLW is to be believed, economic growth will be 0.37% lower in 2035 as a result.

KLW make two other mistakes, adopted from the previous papers linked above. First, they interact the variable of interest with its level. For instance, they explain economic growth, the change in per capita income, by the change in temperature and the change in temperature times the temperature. This makes no sense. Interactions cannot be interpreted if base levels are omitted. They should have explained economic growth by the change in temperature, the temperature, and the change in temperature times the temperature. They justify the omission of the base level because the temperature would be constant. If that were true, we would not worry about climate change; and the base level would be absorbed by the country fixed-effect. It is not true; as a result, KLW’s time fixed-effects are correlated with their variable of interest. Their results are uninterpretable, biased, and cannot be extrapolated.

Second, they assume that a country’s growth rate depends on its temperature. Rich countries are rich because they are cool, poor countries are poor because they are hot.

I had hoped environmental determinism died with Ellsworth Huntington.

Ross McKitrick is a co-author of the report, which cites 16 papers by McKitrick, increasing his citations by 0.4%. Let’s look at three of the cited papers, co-authored with Kevin Dayaratna.

The DoE reports writes: “Uncertainties in ECS [Equilibrium Climate Sensitivity] are highly consequential for policy making. As will be discussed in Chapter 11, economic models use ECS values to project the costs of CO2 emissions. The traditional value (3.0 °C) has typically yielded modest global social costs of CO2 emissions, sufficient to justify some policy actions, but mostly deferred to later in this century. If ECS is very high (above 4.5°C) immediate aggressive emission controls become more imperative, whereas no CO2 emission controls are economically justifiable for ECS below 2.0°C (Dayaratna et al. 2017, 2020).”

In contrast to the suggestion in the DoE report, Dayaratna et al. (2017, 2020) do not show results for point estimates of the ECS.

Dayaratna et al. (2017, 2020) find a mix of social costs and social benefits of carbon, depending on the discount rate used, the assumed distribution of the climate sensitivity, and the assumptions on the strength of CO2 fertilization. The DoE summary omits the social costs, falsely claims that Dayaratna only found social benefits.

The selective omission of some of Dayaratna’s results is inconsistent with other policies of the Trump administration. Carbon dioxide emissions are a social benefit if the discount rate is high and CO2 fertilization large. The President has consistently called for a lower interest rate — implying a lower discount rate.

The benefits of CO2 fertilization fall primarily outside the USA. The first Trump administration lowered the social cost of carbon by excluding the impacts of climate change on other parts of the world.

The DoE report continues: “Obtaining a precise estimate [of the climate sensitivity] is impossible, so policy making needs to account for the uncertainty.”

The Von Neumann-Morgenstern Axioms imply that uncertainty is accounted for by considering the mean of the variable of interest.

Later, the DoE report writes: “Dayaratna et al. (2023) used the updated empirical ECS distribution estimate of Lewis (2022), which assimilates modern instrumental and paleoclimatic temperature records, and allowed for a 30 percent gain in the CO2 fertilization benefit in the FUND model, and found that, even at a low discount rate of 2.5 percent, the median SCC as of 2050 is only $18.67, with a 24 percent probability of the true value being negative. At a five percent discount rate the median SCC value is negative until the mid-2040s and at 2050 was only $0.37 with a 49 percent probability of being negative. Thus, under reasonable assumptions a mainstream IAM using updated scientific inputs yields evidence consistent with the SCC not being significantly greater than zero.”

Again, the DoE report cherry-picks the results by Dayaratna. Just as important, the lightly-reviewed 2023 rejoinder to the 2020 paper switches, without explanation, from the mean to the median. This is just wrong.

The Trump Administration wants to overturn this. The EPA published a legal document. The EPA argues that “GHGs […] impact[] public health and welfare only indirectly and not by its mere presence in the ambient air’’. That is true. Carbon dioxide (CO2) is not toxic. Your lungs are full of it. We worry about CO2 because it changes the climate, an indirect effect.

We often worry about indirect effects. As Einstein noted, it is not the fall that kills you, it is the sudden stop. Pushing someone off a building only indirectly kills them.

The EPA does not take issue with characterizing nitrogen dioxide (NO2) and sulphur dioxide (SO2) as air pollutants, even though these gases too affect public health and welfare only indirectly, after being transformed into nitric acid (HNO3) and sulphuric acid (H2SO4), respectively.

My legally trained friends tell me that the law is impartial. If a line of argument holds for two substances (NO2 and SO2) then it also holds for a third (CO2).

The DoE published a scientific report in support of the EPA’s legal argument. I am cited 3 times, incorrectly all three times. I am not the only one, as reported by Science and Wired.

First, the authors write that “Tol (2017) estimates that the private benefit of carbon is large relative to the social cost.” This paper was never published in a peer-reviewed journal and is therefore not admissible by the rules of the US government. The paper was peer-reviewed and rejected, because my private benefit is an average whereas the social cost is a marginal. The two cannot be compared (unless you make a ridiculous assumption about linearity). I still hope to fix the paper one day. As it stands, however, the comparison is wrong.

The authors further write that “[a]n influential study in 2012 suggested that global warming would harm growth in poor countries but the finding has subsequently been found not to be robust (Tol 2024). Studies that take full account of modeling uncertainties either find no evidence of a negative effect on global growth from CO2 emissions or find poor countries as likely to benefit from it as rich countries.” The authors did not read my paper carefully. The Dell/Jones/Olken result is indeed not robust: Later studies found that the differentiation is between hot and cold countries rather than rich and poor ones; or that everyone is affected equally. But the DJO result is indistinguishable from the pooled estimates of all later studies with a comparable specification. If there is one robust paper in this part of the literature, it is Melissa’s.

The second sentence is wrong. The authors refer to “studies” but without references. Tol (2024) finds that the then available studies jointly point to a negative impact of climate change on global economic growth. My less systematic reading of the literature since has not led me to change my mind.

Their conclusion that “poor countries” are “likely to benefit” is again not backed up with references. Tol (2024), the only reference in the paragraph, concludes the opposite.

The third citation is to FUND in the section on the social cost of carbon. The literature is vast. I counted 446 papers with estimates. There are numerous commentaries; and two handfuls of meta-analyses (e.g. Tol (2023) and Moore et al. (2024)). Instead, the authors wrote their own review, which omits the most influential papers and misses key insights. Cherry-picking may be a better term than review.

Strikingly, in all three examples of twisting the literature on the economic impact of climate change, the authors do not argue that there would be no effect, or a positive one. Even their “poor countries [are] as likely to benefit from it as rich countries” is best read as “not likely at all” if “it” refers to climate change and the sentence is based on Kahn et al. (2021).

If, instead, “it” refers to carbon dioxide emissions then indeed cost-benefit analysis of climate policy calls for a gradual decarbonisation. That is, in the short run — but not in the long run — the benefits of fossil fuel combustion outweigh its costs. More importantly, we should reduce emissions now.* Complete decarbonisation in the long run requires partial decarbonisation in the short run.

In other words, this attempt by the DoE to undermine the economic case for climate policy fails — and thus inadvertently strengthens said case.

*Actually, we should have started cutting emissions in the 1970s and did. Reagan was wrong to reverse Carter’s policies.

Since then, previously overlooked impacts have come to light. Notably, heat does not just affect retired but working people too. In hot and humid Southeast Asia, a few degrees of warming would reduce labour productivity, and hence wages, by 25%. This is now the dominant impact in some models.

New discoveries follow the usual cycle: Careful follow-up studies show that the initial spectacular result is not quite what it seemed. This is perhaps best illustrated with the paper by Burke, Hsiang and Miguel, who claimed that climate change would have large and permanent effects on economic growth, dwarfing all previous assessments. While this paper continues to be embraced by some, others have pointed to its methodological deficiencies that led Burke and colleagues to overstate their case by two orders of magnitude.

Thirty years after AR2, the central estimate is that climate change will have a modest impact on average human welfare. A century of climate change is about as losing a year of economic growth. Impacts are much larger in poorer countries, but the most severe impacts are mostly due to underdevelopment and mismanagement. Large impacts in poor, populous countries add up to modest global impacts because the average counts dollars, not people. The uncertainties are large and skewed: Negative surprises are more likely than positive ones.

Estimates of the social cost of carbon illustrate just how uncertain. IPCC AR2 reports a range of estimates spanning 4 orders of magnitude: 0.3 to 154 $/tC. A recent meta-analysis spans 11 orders of magnitude: -1,000 to +100,000,000 $/tC. Research over the last 30 years has been more about discovering the dimensions of the problem that climate and climate change pose to economies than about estimating the size of the problem.

A number of recent, yet-to-be-published papers illustrate this. Properties are threatened by fire, water, and wind. This is not a big deal in advanced economies because such damages are insured. While a single event can cause what seems like eye-watering damage, insurance premiums are tiny. Natural disasters are locally large but globally small. However, insurance companies are now withdrawing cover in certain markets. The risk is no longer diversified but concentrated. Homes are by far the largest component of household wealth; and mortgages are a big part of banks’ balance sheet. Without property insurance, a large natural disaster can cause a banking crisis. Regulatory failure magnifies the impacts of climate change.

Many people express skepticism at the economic impact functions used in integrated assessment models for cost-benefit analysis. The modest impacts for high warming draw particular ire. This is a red herring. The assumed impacts are sufficiently large to justify greenhouse gas emission reduction to keep temperatures well below 3 degrees Celsius of warming. The assumptions about what would happen at 4 degrees may be spectacularly wrong, but they do not influence the model results.

Overall, research on the economic impact of climate change is still largely an academic endeavour. Firm advice on the desired intensity of climate policy is well beyond our current understanding. When asked for policy advice, I prefer to focus on the design of climate policy, where economists are on much more solid ground.

Economics and finance are different disciplines. Publishing both in the same journal is strange. I once proposed to split the journal into two but the tyranny of journal impact factors stopped that: The Institute for Scientific Information (now Clarivate) does not allow daughter journals to inherit their mother’s reputation. So we continued to publish in finance.

We are now revisiting that decision. Our historical obligation to finance is 25 years old and fading. Older finance journals now regularly publish in what used to be almost exclusively our area. New finance journals appeared to satisfy supply.

The credibility revolution in economics seems to have passed by finance. Most finance papers are about associations. The econometrics are complicated and advanced, often cutting-edge, but the results are descriptive. You can make your descriptive statistics as fancy as you like, but you still do not have a causal interpretation. We used to worry about parameter instability. Now people proudly present time-varying parameters — models that are incomplete by construction.

Replication packages have become common in economics in the wake of Jaap Abbring’s bold experiment with Quantitative Economics. It has yet to be widely accepted in finance. Our replication audits reveal that many researchers lack the training to write a proper replication package while some show an unwillingness to engage. Too many focus on the volume of their output rather than the quality.

Problems with integrity — skullduggery, fakery, bribery — also appear to be concentrated in finance.

The editorial board of Energy Economics has therefore decided to reduce the number of finance papers considered for publication. We will publish some finance but not nearly as much as in the past.

CBA is a good way to get published in an economics journal. It is less suitable for climate policy advice. A CBA tells a philosopher-queen what to do, but it is an academic exercise until we endow Lady Gaga with her rightful powers. She would find it better to steer on price than quantity, a robust, near-consensus finding in the academic literature that our actual leaders have studiously ignored.

Politicians favour quantities, and in recent years, dates for net-zero. Promising that your successor’s successor will meet your goal may be good politics, but policies matter more than targets. I aim to lose weight. I’ve set a target: 78 kg. That does not help. More exercise, less food does.

Targets can, of course, be used to guide policy. Say you want to reach zero emissions of carbon dioxide by 2070. Coal-fired power plants have a life-time of 40 years or more. Any plant that goes online in 2030 will need to be capture-ready so it can be zero-emission in 2070. Planning and building a power plant takes 10 years, so regulations will have to be in place by 2020. Oopsy.

(I know. It is cheaper to close the plant a year early. But you would like to know this when you start planning.)

Politicians have set targets and timetables for greenhouse gas emissions for 40 years, since 1985 — and missed most (some were hit by accident). Emissions have continued to rise. Climate policy has been a resounding failure if you define “success” as “target met”.

At the same time, we have seen a revolution in the energy supply — first, shale oil and gas, then wind and solar, and now batteries. Alternative proteins and new rice varieties may do something similar to the food supply. Projected emissions have fallen, and actual emissions may be about to peak.

This suggests that useful targets are for the commercialisation of carbon-free alternatives. Renewables are beyond grid parity, electric vehicles are almost there. One aim is for people to want electric heating — rather than being forced to. More importantly than establishing that aim is answering the question what needs to happen to cost, performance and supporting infrastructure to get there. That’s what successful climate policy did in the past, and that is what it needs to focus on in the future.

This can be misinterpreted as a call for micromanagement. It is not. Politicians are good at politics, civil servants at civil service. What is needed is broad support for pre-commercial research and development and sufficient regulatory stability for investors to want to punt on a future product. A carbon price may help to stabilize regulation. Cost-benefit analysis would help to decide which targets are worth pursuing. But, at the end of day, the climate problem will be solved by entrepreneurs putting carbon-neutral products on the market that people will want to buy regardless of the climate.

The spread of academic expertise across the globe is a good thing. Students get taught better closer to home. Policymakers get better advice. But there are downsides too. I discuss three: language, money, and defence.

Research is broadcast in English. I have studied English as a second language for 50 years, while my native tongue is similar. Other people are not so lucky. Expressing yourself clearly in a foreign, almost alien language is hard. Fortunately, AI tools are getting better but at the risk of standardizing expressions and raising the prospects of unintended plagiarism.

There are other concerns, though. AI is terrible at jargon (as are human language editors, whose specialty is English). If you do not really understand the meaning of words, then it is easy to mistake one data series for another. We have seen an increase in the number of cases where authors swap one variable for another; or argue that one series is a proxy for an unrelated one.

“When I use a word,’ Humpty Dumpty said in rather a scornful tone, ‘it means just what I choose it to mean — neither more nor less.’
’The question is,’ said Alice, ‘whether you can make words mean so many different things.’”

We are not in Wonderland and these are not proxies. I think we should call them HumptyDumpties.

Money is the second issue. Relationships between authors and between authors and editors have always been fraught — frequently around patronage, sometimes around sexual favours — but this used to be local. The internet has destroyed distance, the pandemic has shattered localities. A new factor has thus come into play: Large disparities in income. Universities in East and West Asia now pay substantial publication bonuses, with poorly paid but well-educated scholars in South Asia in between. I think this is a minor offense: The quality of research is more important than who pretends to have conducted it.* That said, if your name is not on your paper, then you do not suffer the reputational loss if the paper is found to be fraudulent.

Editors are not well-paid either, one reason why publishers are so very profitable. There have certainly been failed attempts to bribe editors — there are persistent rumours that some of these attempts succeeded.

This brings me to the third and final issue. Rob Socolow once described Princeton as an old university with many lines of defense. (Princeton is from 1746 but then Rob is from the USA.) We have seen the emergence of many young universities with presumably few lines of defense, in countries with a short history of academic research and regulators who do not really understand the sector. At Energy Economics, we reject papers if we suspect fraud. We only notify the university of the authors if we have evidence of fraud. Many universities acknowledge receipt, some report back on the results of the internal investigation. But there are also universities that seem to ignore reports of misconduct by their employees — and I was once attacked by a Head of Department for daring to question the integrity of her faculty.

Editors are gatekeepers. We are also busy. Our main objective is to protect the reputation of ourselves and our journals. We would rather mistakenly reject a good paper than mistakenly accept a bad one. Although we remain wedded to the policy of judging a paper on its quality and its quality alone, the misbehaviour of entire departments sometimes makes me doubt the wisdom of that policy.

*Clarification: Authorship is irrelevant for science. Calculus is great, whether invented by Newton or Leibniz. Authorship matters greatly to authors and their employers. Adding fake authors to your paper dilutes your status and diminishes the reputation of your university. Adding your name to a paper you did not write is embellishment that violates labour law.

Het Burgerberaad is een citizen jury. Het parlement delegeert een taak aan een willekeurige groep burgers die kennis verwerven, discussieren, en een gezamenlijk advies geven. Citizen juries worden te pas en te onpas gebruikt. Dit Burgerberaad gaat over de bijdrage van huishoudens aan het klimaatbeleid. Het is goed dat burgers hier over gehoord worden. Het is belangrijk dat die burgers toegaan hebben tot alle relevant informatie, zonder filter op politieke kleur.

Acht Maart was het zover. Twee gesprekken van een uur met mensen die nieuws- en leergierig waren. Iedereen was beleefd en constructief. Ik had liever langer gepraat. Daarna nog een interview over mijn indrukken en, heel vreemd, mijn relatie met Professor Reyer Gerlagh. De vraag bevestigde mijn eerdere indruk dat er iets niet in de haak was. Reyer is een oud-studiegenoot en -collega. Ik zie hem altijd graag. Hij heeft een ander perspectief, altijd doordacht, en ik leer daarvan.

Ik was het al weer bijna vergeten toen Marcel Crok contact opnam. Marcel is een wetenschapsjournalist die meer weet over klimaatverandering dan de meeste journalisten (en enkele klimaatwetenschappers). Marcel is terecht kritisch over sommige dingen die gaande zijn in klimaatonderzoek.

Het Burgerberaad had drie uitspraken van Marcel met de “feiten” vergeleken en “gecorrigeerd”. Marcel heeft uitgebreid op de eerste twee gereageerd. Hij heeft gelijk en het Burgerberaad ongelijk: Er is geen klimaatsignaal waargenomen in orkanen en overstromingen. Dit is deels omdat oudere waarnemingen van lage kwaliteit zijn en deels omdat er zo veel andere dingen gaande zijn, met name voor omstromingsrisico’s.

Marcel’s derde uitspraak is mijn kernexpertise: Zijn de baten van het Europese klimaatbeleid groter dan de kosten? De Europese doelstellingen zijn moeilijk te rechtvaardigen, maar de huidige prijs van uitstootrechten is goed te verdedigen. Het Europese klimaatbeleid is onnodig gecompliceerd en duur; de effectiviteit is omstreden. Marcel’s skepsis is in orde.

Het Burgerberaad meent van niet. Ik heb daar drie problemen mee. De organisatie van het Burgerberaad zou neutraal moeten zijn. Het gaat om de mening van de burgers, niet van de organisatoren. Het Burgerberaad gaat over Nederlandse huishoudens, niet over het hele Europese klimaatbeleid.

Het derde probleem is het grootst. In de conceptversie van de factcheck haalt het Burgerberaad een studie van Sanderson & O’Neill aan. Zij concluderen dat “whereas 18 (5) percent of optimal solutions starting in 1980 meet the 2 (or 1.5) degree target, 5 (or 0)% of 2020 simulations meet the goals”. Met andere woorden, de kosten van de twee graden doelstelling zijn groter dan de baten in 19 van de 20 gevallen; de kosten van de anderhalve graad doelstelling zijn altijd groter. Deze conclusie is geen uitbijter in de literatuur.

Sanderson & O’Neill schrijven verder dat het beter geweest zou zijn als de uitstootbeperking in 1980 begonnen was (onder premier Dries van Agt). Het is nu 2025. De Verenigde Staten hebben zich weer teruggetrokken uit het internationale klimaatbeleid, China doet weinig, en India minder. Er was een kleine kans in 2020, een kleinere in 2025.

Het Burgerberaad beweert dat Marcel ongelijk heeft en, in de onderbouwing, haalt een studie aan die Marcel gelijk geeft.

Dat was het concept. Marcel heeft ze op de juiste lezing van Sanderson & O’Neill gewezen. In de gepubliceerde versie wordt Sanderson & O’Neill verzwegen.

Het is niet toegestaan een studie bewust te verzwijgen die je conclusie tegenspreekt.

Het bovenstaande is herhaaldelijk besproken met het Burgerberaad.

Update (14 November 2025): Na lang gesteggel heeft het Burgerberaad eindelijk Marcel Crok toegestaan een tegenwoord te schrijven: https://www.burgerberaadklimaat.nl/kennis/documenten+kennis/factcheck+documenten/handlerdownloadfiles.ashx?idnv=3027145

Neutraal kun je deze halstarige vertraging niet noemen.

Incentive pay is a good thing. At Vrije, we get teaching relief proportional to last year’s publications and a phone call from the rector if we get into the Big 3. (Sussex is less generous.) Other universities pay cash. The largest number I’ve heard is $10,000 per paper.

Numbers that large demand vigorous enforcement of ethical standards. We recently caught a PhD student. He had written a sound paper on his own and sold five co-authorship positions. That is one way to finance your studies.

Editors are in a better position. I could have retired from admin had I taken the offer below. Instead, I reported the authors and shut the scam down.

Rumour has that some editors have given in to temptation.

On 20 November 2024, I got the following email:

Subject: Proposal for Long-term Collaboration in Manuscript Solicitation

Dear Editor,

I hope this email finds you well.

I am the manager of [redacted] in America. One of our businesses is to solicit manuscripts from diverse regions all over the world, and recommend them to suitable academic journals.

I'm writing to you to discuss the potential cooperation with “Energy Economics”. The main content of our cooperation is that we solicit and recommend papers to you to publish in your journal if they meet your requirements, even better, within a short period.

If you are interested in this cooperation, we can submit related articles according to your requirements, and the number of articles will be as required. Of course, if you are willing to receive more papers, we will be pleased to submit more.

We are open to discuss more details. I look forward to your reply.

The email was signed Vinnie. We continued our discussion on WhatsApp.

27/11/2024, 09:59 - Richard Tol: Vinnie: This is Richard Tol. A guaranteed publication in Energy Economics costs $5000. As I said, I will need to see a sample paper first.

27/11/2024, 10:06 - Vinnie: Okay, sir. Could you please tell me how long the review time, online time, and index time?

27/11/2024, 10:07 - Richard Tol: We need to keep up pretenses so review 6 months. Online and index is a matter of days.

27/11/2024, 10:11 - Vinnie: Will I pay you 5000 dollars after accepting the paper?

27/11/2024, 10:11 - Vinnie: paypal

27/11/2024, 10:11 - Vinnie: ?

27/11/2024, 10:12 - Richard Tol: No cure no pay: Payment upon conditional acceptance / minor revision.

27/11/2024, 10:12 - Richard Tol: PayPal is fine

27/11/2024, 10:13 - Vinnie: ok ,payment after acceptance is very

27/11/2024, 10:13 - Vinnie: This message was deleted

27/11/2024, 10:13 - Vinnie: good

27/11/2024, 10:14 - Vinnie: i can submit many papers

27/11/2024, 10:14 - Richard Tol: Please do

27/11/2024, 10:14 - Vinnie: Can you recommend a writing team to me?

27/11/2024, 10:15 - Richard Tol: No. That's your job.

27/11/2024, 10:16 - Vinnie: how much is the APC?

27/11/2024, 10:16 - Richard Tol: O

27/11/2024, 10:17 - Richard Tol: 0 even

27/11/2024, 10:19 - Vinnie: When revising a manuscript, do we replace the author's name before it is accepted?

27/11/2024, 10:20 - Richard Tol: No, that would set off every alarm.

27/11/2024, 10:22 - Vinnie: Can you help review the topic in advance to see if it meets your submission scope?

27/11/2024, 10:24 - Richard Tol: Scope is on the web: economics and finance of energy and environment

27/11/2024, 10:26 - Vinnie: ok,i will submit very soon

27/11/2024, 10:47 - Vinnie: Dear sir, you can review the manuscript in first, right? Can all manuscripts after submission be accepted?

27/11/2024, 10:48 - Richard Tol: First paper on Gmail. After that, normal submission with notification on WhatsApp

27/11/2024, 11:02 - Vinnie: OK sir

27/11/2024, 11:02 - Vinnie: I will give you about 10 titles.please review them first

28/11/2024, 08:04 - Vinnie: 1. Financial agglomeration and financing constraints in enterprise green technology innovation: theoretical mechanism and empirical test;

2. The enhancing effect of green financial development on the transformation and upgrading of industrial structure: the mechanism of technological innovation;

28/11/2024, 08:05 - Vinnie: 3. Research on the synergistic impact of environmental regulation and digital economic development on green all-factor energy efficiency

4. ⁠4. Research on the impact and mechanism of environmental tax reform on energy utilization efficiency: the regulatory role based on economic aggregation

5. Multi-perspective integration of corporate social responsibility, digital economy and enterprise energy utilization efficiency

28/11/2024, 08:06 - Richard Tol: Titles are fine, but anyone can come up with a decent title. I need to see papers and authors.

28/11/2024, 08:09 - Vinnie: If the topic is within your scope, I will start to contact the author.

28/11/2024, 08:10 - Richard Tol: Please do

28/11/2024, 08:17 - Vinnie: Ok

04/12/2024, 03:59 - Vinnie: dear sir，The author has already confirmed it.

Financial Agglomeration and Financing Constraints in Enterprise Green Technology Innovation: Theoretical Mechanisms and Empirical Tests--------Does it comply? Can he start writing?

04/12/2024, 20:33 - Richard Tol: Yes go ahead

05/12/2024, 01:09 - Vinnie: ok sir

16/12/2024, 01:30 - Vinnie: Dear sir.What is the plagiarism rate requirement?

16/12/2024, 05:59 - Richard Tol: 20% or less

16/12/2024, 07:38 - Vinnie: ok sir

16/12/2024, 11:21 - Vinnie: Dear sir, I have prepared several manuscripts and will be ready to submit them in a few days.Can the first few manuscripts be quickly hired and online, so that the authors can trust me later and I can submit a large number of manuscripts? I can submit about 500 manuscripts every year.

16/12/2024, 11:22 - Vinnie: I can pay extra for the quick publication of the first few manuscripts online in advance.

16/12/2024, 11:24 - Richard Tol: Please submit and we'll take it from there

16/12/2024, 11:51 - Vinnie: OK sir

16/12/2024, 11:56 - Vinnie: Dear sir.Do manuscripts need real data?

16/12/2024, 11:56 - Richard Tol: Nah

18/12/2024, 09:01 - Vinnie: dear sir ,are you there?

18/12/2024, 09:02 - Vinnie: <Media omitted>

18/12/2024, 09:02 - Richard Tol: Yes

18/12/2024, 09:03 - Vinnie: The author is ready to contribute. Is it submitted by the website system? Or contribute by email?

18/12/2024, 09:03 - Richard Tol: Website

18/12/2024, 09:04 - Vinnie: ok,I'll send you the receipt number after the author submit.

18/12/2024, 09:05 - Richard Tol: 👍

19/12/2024, 03:37 - Vinnie: Dear sir,the number is ENEECO-D-24-03698

19/12/2024, 03:38 - Vinnie: Please give it a quick review and accept it. I need to gain the trust of the authors. Thank you.

19/12/2024, 03:39 - Vinnie: Please let me know at any time if you need extra expenses.

That changed when Oxford University Press raised the price of a Kolstad paperback from £30 to £130. That’s right. They raised the price by £100. For the same, aging product. That’s monopoly power. I cannot in good conscience ask my students to pay that, so my complement-to-Kolstad became a substitute-for-Kolstad. Sorry Charlie.

Last year, I gave the students a freshly written first draft. Now it’s a finished manuscript. What to do?

I published my previous textbook on the economics of climate change with Edward Elgar. They offer some services, like proofreading and publicity. Updates are every 4-5 years. The next edition is scheduled for publication in 2027. They charge a lot for that. The book sells for £30. I get £1. (The royalty scheme is non-linear and segmented, but £1 is basically it.)

I do not see why my students should fork over money to the shareholders of a company that does not pay me for my labour. So I decided to try something else instead. The new book is published in five ways:

• The latest version can be had for $10. This is cheaper than any other textbook in environmental economics. It’s a PDF and I fully expect students to share. I can update when I see fit.

• There is a paperback on Amazon for those who do not like reading on screens.

• There is a hardcover on Amazon for the library. This also means that the book will pop up in search engines.

• An earlier version is available for free on GitHub. There is an honesty box for voluntary contributions.

• Instructors can ask for a PDF with the solutions manual.

This is an experiment. I may regret my choices and reverse course. The money made (which is more per book despite its lower price) will support research.

I updated Figure 2 in that paper:

How do they get there? The main regression yields a Jorda-like impulse response function. They regress the growth rate of per capita income, averaged over one to ten years, on temperature. This is a Dell-like specification. The regression is unbalanced: A flow is regressed on a stock. This works in-sample as fixed-effects compensate for the imbalance. As a result, the fixed-effects are correlated with the variable-of-interest, which hampers interpretation. Fixed-effects do not help out-of-sample so that models like this do not cross-validate.*

If economic growth depends on the level of temperature, then growth will be permanently higher or lower if climate stops changing. Use this model for deep history and you’ll find that poor countries are poor because they are hot. This is the sort of climate determinism that would make Ellsworth Huntington blush.

In this, Bilal and Kaenzig repeat the mistakes of Dell, Burke, and Wenz. They add a twist, which explains why they are even further out. Instead of using temperature, Bilal and Kaenzig use temperature shocks, which they define as temperature deviating from its recent trend. In the period from 1950 to 2023, temperature shocks fall between -0.2K and +0.2K. That is, their central forecast — 12% for +1K — is five times larger than their largest observed shock. I once, unsuccessfully, tried to coin the term “ultrapolation” for what lies beyond extrapolation.

The temperature shocks of Bilal and Kaenzig look remarkably like El Nino / Southern Oscillation.** The economic effects of ENSO have been studied extensively. It is damaging because normal weather patterns are disrupted for a short time. Climate change is different. It is permanent, giving people, companies, governments, institutions, and technologies time to adapt to the new circumstances. As too many others before them, Bilal and Kaenzig estimate a short-term elasticity and confuse it for a long-term elasticity.

*Update: Bilal and Kaenzig also estimate a model without fixed-effects. The results are roughly the same. That misses the point, though. Whereas Burke uses fixed-effects to rebalance a misspecified equation, Bilal and Kaenzig uses local trends for the same purpose. This works fine in-sample but not out-of-sample. For this model to work out-of-sample, they would have to re-estimate their local trend for every realization of a stochastic model of future temperatures. Section 4.2 reveals that they do not do this.

**Update: In the November 2024 version of the paper, Bilal and Kaenzig add a control for ENSO to their main specification. This has little effect. This is hardly surprising. If their temperature shocks look remarkably like ENSO, then adding ENSO as a control does not add new information.

Some academics feed this fear. Tim Lenton is one. My paper responds to Xu et al. (2020), repackaged as Lenton et al. (2023). In these papers, the authors place many future people outside the range of past climates, and argue that this is dangerous.

I have three objections. The first is philosophical. Unprecedented does not mean bad, as Hume and Moore noted. We experience many things that no human has ever experienced before — some are good, some are bad — and there are unprecedented things I long for — a world without poverty and bigotry is high on my list.

The second objection is statistical. The papers by Lenton and colleagues are about what happens if people would move beyond the current extremes. This is a question about the tails of the distribution. Lenton, however, assumes a mixture of normal distributions, a method well-suited to study the central tendency of a population. Univariate extreme value statistics is well-established, but climate is multivariate (although only temperature and rainfall are considered). I therefore had to cobble together a new estimator: Dominicy’s multivariate generalization of Hill’s estimator of the tail-index, applied to Worton’s proposed multivariate order statistics. For good measure, I also throw in a QQ-estimator of the tail-index because it can be readily corrected for top-censoring (dubbed Tobin’s QQ). This satisfies the statistician in me, but the results indicated that the tails of the distribution of the human population in climate space are thin. Lenton assumed this. I show that this is a fine assumption.

My third objection is substantial. Lenton assumes that 10% of the world population are located in unliveable climates. Yet they are alive. Climate change will move some 20% of the world population into Lenton’s areas of the undead. This drives the difference in results: Lenton puts billions of future people in unprecedented climates, whereas I find that it is hundreds of millions.