July 21, 2015

Did farmers in the Eastern US adapt to climate change?

Better ask first if climate has changed in the Eastern US.

A note on the the long-difference method – Burke and Emerick (2013)

Burke andEmerick (2013) study if corn and soybeans growers in the Eastern US have adapted to climate change from 1980 to 2000. Instead of climate they consider five-year weather and crop yield averages from 1978 to 1982 and from 1998 to 2002. For each county they calculate the differences of average yields between the five-year averages centered on 1980 and 2000 and regress it on the difference between 1980 and 2000 of the average number of degree days below and above 29 °C during April-September. They find that the coefficient of degree days above 29 °C is negative and significant, as expected. However, the coefficient is not significantly different from the coefficient estimated using a traditional panel model with fixed effect. It thus seems that the response function of yields is the same whether it is estimated using weather fluctuations or longer term temperature changes. They argue this is evidence of lack of adaptation.

I argue instead that this is just what one would expect to observe. Because climate has not changed in the Eastern US. Burke and Emerick (2013) captures noisy weather signals rather than a stable climate pattern.

See here for a longer discussion, references to the scientific literature and maps of climate patterns in the Eastern US.

Adaptation to extreme heat?

A note on the interpretation of results in Deschênes and Greenstone (2011) by Dell, Jones, and Olken (2014)

Deschênes and Greenstone (2011) use interannual weather fluctuations to identify the effect of temperature on mortality. They find that days with temperature above 90 F sharply and significantly increase the mortality rate. Note that days with mean temperature above 90 F are very rare. In several regions the number of days is close to 0.1 (one day every ten years on average).

Deshênes and Greenstone divide the sample in nine regions and repeat the panel estimate for each of them. (Dell, Jones, and Olken 2014)regress the nine regional coefficients of temperature above 90 F on the average number of days in which temperature above 90 F is observed in each region.

One would expect a significant negative relationship, indicating that regions with more extreme temperature events have adapted at the extensive margin to reduce mortality. However, they do not find a significant relationship and this is taken as evidence of lack of adaptation. This conclusion is questionable.

The regional regressions reveal that days with temperature above 90 F are significantly harmful only in regions where the extreme temperatures are observed with some frequency. In the other regions the estimates are not precise and sometimes the coefficients are negative, which is a counter-intuitive result. The estimates of six of out nine coefficients are thus not precise. It is not a surprise that Dell, Jones, and Olken (2014) do not find a significant relationship and this should not be taken as evidence that hottest regions do not adapt to the extreme temperatures.

For a more detailed discussion see here.

February 16, 2015

Will climate change increase or decrease migration in rural Africa?

In a recent working paper Cristina Cattaneo and I examine how climate affects migration decisions at the household level in rural Ghana and Nigeria. Contrary to most of the other papers in the literature, we deal with climate - i.e. the long-run average of weather - rather than with climate shocks.

Is migration an adaptation that households in Ghana and Nigeria use to cope with current climate?

If the answer is yes, it is reasonable to expect that migration will also be an adaptation to future climate change.

The data to test these predictions are drawn from two different household surveys: the Nigeria General Household Survey and the Ghana Living Standard Survey. We find a hill-shaped relationship between temperature in the dry season and the propensity to migrate in households that operate farms. We also find a significant hill-shaped relationship between precipitations in the wet seasons and the propensity to migrate in farm households. Climate has instead no significant impact on the propensity to migrate in non-farm households. Climate change scenarios generated by General Circulation model reveal that, ceteris paribus, migration may decline in Ghana and in Nigeria.

I copy below maps of marginal effects of temperature and of precipitations on the probability of a household to have at least one migrant member.







Cattaneo, C. and E. Massetti. 2015. “Climate and Migration in Rural Ghana and Nigeria.” FEEM and Georgia Institute of Technology, mimeo.

December 18, 2014

Should we ban unconventional oil extraction to reduce global warming?

We all know that unconventional oil extraction is bad for the environment. It causes local environmental damage (check what is happening in North Dakota: here) and it generates a lot of extra carbon dioxide emissions compared to the extraction of conventional oil.

So, what about banning the extraction of non-conventional oil?

In a recent working paper  Samuel Carrara and I estimate the climate benefit of a global ban on unconventional oil using scenarios developed with the integrated assessment model WITCH.

Guess what? A global ban on the use of unconventional oil has non-negligible climate benefits but it is a very inefficient climate mitigation policy. Not using unconventional oil resources slows global warming by 0.3°C (from +4.1°C to +3.8°C in 2100 with respect to the pre-industrial level). Despite a rebound effect in conventional oil extraction, the global ban is effective because it substantially reduces oil demand, carbon dioxide emissions and the increase of temperature. However, the policy is terribly inefficient.

We find that an efficient, global uniform carbon pricing mechanism would achieve climate benefits almost four times larger, at the same cost. Analogously, an efficient pricing scheme would deliver the same climate benefit being fifteen times cheaper.

The EU has long considered a unilateral tax on oil coming from unconventional resources in Northern America.

Is this a good idea?

We check what would be the cost and the benefit (in terms of reduced carbon dioxide emissions) of a unilateral EU ban on unconventional oil. Unsurprisingly, we find that unilateral European ban of unconventional oil is both inefficient and ineffective. It will cost a lot and it will have no impact on global mean temperature. Oil would just flow to countries that do not ban its use.

The policy implications are intuitive. If the main goal is carbon mitigation, the European Union should avoid unilateral aggressive policies against unconventional oil. Diverting trade routes may be expensive for oil producers and a short-term victory is possible. However, in the long-run, with rising energy prices and technological progress in oil extraction and in oil transportation, it is likely that unconventional oil will flow where demand is and Europe alone will have a negligible impact on global patterns.

For example, starting a trade war with Canada to achieve virtually null climate benefits is not the best thing to do.

December 11, 2014

Using Degree Days to Value Farmland

A final draft of the working paper joint with Robert Mendelsohn and Shun Chonabayashi is available here.

In this paper we carefully review the use of degree days in the hedonic literature to value farmland and we are not able to confirm the hypothesis of Schlenker, Hanemann and Fisher (2006).


Here is the abstract:

Farmland values have traditionally been valued using seasonal temperature and precipitation. A new strand of the literature uses degree days over the growing season to predict farmland value. We find that degree days and daily temperature are interchangeable over the growing season. However, the way that degree days are used in these recent studies is problematic and leads to biased and inaccurate results. These new findings have serious implications for any study that copies this methodology.


The Appendix to the paper has a careful comparison of the weather data that we use (NARR) and data used by Schlenker and Roberts (2009). For those not familiar with the acronyms, NARR is the North American Regional Reanalysis generated by climatologists at the NOAA. It provides temperature and other climatic measurements over a 32x32 km grid at three hour time intervals from 1979 to present day.

Wolfram Schlenker took great care in examining previous drafts of our work (here, here and here). He has compared NARR data to his dataset and he has found that NARR data is inferior. But that comparison was not correct.

Instead of using NARR 2 meter air temperature, Schlenker used NARR  surface level temperature. This is like comparing pears and apples because Schlenker and Roberts (2009) - SR2009 - is based on weather stations. Weather stations record temperature at about 2 meters. All temperature data that is used in this literature is 2 meter air temperature and we never used surface temperature data. Surface temperature is the temperature of the “skin” of the planet. Surface temperature reflects different soil types. Daily maximum temperature can be very high in the NARR surface temperature dataset (try touching your concrete driveway on a summer afternoon). For this reason the NARR data seems inferior.

In short, both NARR and SR2009 data confirm that Schlenker, Hanemann and Fisher (2006) (SHF2006) greatly overestimate the number of degree days above 34°C. SR2009 and NARR data are quite similar (Wolfram Schlenker kindly gave us his weather data). In some tests NARR data performs better than SR2009 data, but I would not overstress this. The relevant fact is that with both NARR data and SR2009 data we reject the main hypothesis in Schlenker, Hanemann and Fisher (2006) - SHF2006. Why?

We find two problems with SHF2006. First, the weather data used in that paper is not as accurate as in the NARR and the SR2009 datasets and this may have misled the authors. Second, SHF2006 misinterprets agronomic research: farmers and agronomists do not use degree days to predict yields (and thus overall agricultural productivity). The argument in favor of degree days is based on a misreading of an agronomy result showing a linear function rising to 32°C and then abruptly falling (Figure 2-3 in Ritchie and NeSmith, 1991). However, the cited figure does not describe yield but rather the inverse of the time it takes a maize plant to develop a fifth leaf. The figure shows how degree days affect timing. A separate figure in the Ritchie and NeSmith paper reveals the traditional hill-shaped relationship between yield and temperature. In fact, farmers and agronomists use degree days to predict the duration of different stages of plants' growth, not to predict yields.

Finally, we also checked if using hourly temperatures instead of daily temperature to calculate degree days makes a difference and we are able to confirm our results, but this is technical stuff and all the details are in the Appendix.

July 24, 2014

Rethinking African solar power for Europe

Recent column with Elena Ricci on voxeu.org.

"Concentrated solar power generation in Northern African and Middle Eastern deserts could potentially supply up to 20% of European power demand. This column evaluates the technological, economic, and political feasibility of this idea. Although concentrated solar power is a proven technology that can work at scale, it is currently four or five times more expensive than fossil fuels. Concentrated solar power could play an important role in Europe’s energy mix after 2050, but only if geo-political challenges can be overcome."

Full column available here.


June 26, 2014

Revised Working Paper: A Ricardian Analysis of the Impact of Climate Change on European Agriculture

We recently revised our paper on climate change impacts on European Agriculture.

The new FEEM working paper is available here.


This new version is also circulated as CESIfo working paper here.


Abstract:

This research estimates the impact of climate on European agriculture using a continental scale Ricardian analysis. Climate, soil, geography and regional socio-economic variables are matched with farm level data from 37,612 farms across Western Europe. We demonstrate that a median quantile regression outperforms OLS given farm level data. The results suggest that European farms are slightly more sensitive to warming than American farms with losses from -8% to -44% by 2100 depending on the climate scenario. Farms in Southern Europe are predicted to be particularly sensitive, suffering losses of -9% to -13% per degree Celsius.

New Draft: Do Temperature Thresholds Threaten American Farmland?

New draft of the paper on agricultural thresholds presented at the 2014 ASSA meetings.


I will present this new draft at the World Congress of Environmental and Resource Economists in Istanbul on Monday June 30 at 14:00.


Robert Mendelsohn and I do not find evidence of "thresholds" after which land values collapse in the East of the United States. We find instead evidence of adaptation to different climatic conditions.



Abstract:

It is widely known that temperatures have a hill-shaped effect on agriculture.  Some researchers argue that there is also a threshold effect, a temperature above which land values crash and crops fail. This paper uses flexible functional forms to estimate the effect of growing season temperature on American farmland values and crop yields. The paper finds evidence of the hill-shaped response function for both farmland value and crop yields. But there is no evidence of temperature thresholds whether temperature is measured at 3 hour intervals, daily, or for multiple days.

June 20, 2014

Book: Climate Change Mitigation, Technological Innovation and Adaptation


Finally, the long-due book that summarizes work done with the integrated assessment model WITCH
Abstract and contributors below, more information and order form here.




Abstract

This book presents a rigorous yet accessible treatment of the main topics in climate change policy using a large body of research generated using WITCH (World Induced Technical Change Hybrid), an innovative and path-breaking integrated assessment model.

The authors give a particular emphasis to the analysis of technological change necessary to build low-carbon economies. The WITCH model can track all of the actions which impact the level of mitigation – such as R&D expenditures, investments in carbon-free technologies and adaptation, purchases of emission permits, or expenditures for carbon taxes – thus allowing for the evaluation of equilibrium responses stimulated by different climate policy tools. The chapters examine various questions to explore the future of climate change policy. Why is it so hard to achieve a global agreement that paves the way to widespread reductions of carbon dioxide and other greenhouse gas emissions? What are the technologies that would deliver clean energy without harming economic growth? And finally, how does uncertainty about future policies and future technologies affect choices in the present?

This innovative book will appeal to researchers, policy makers and academics interested in climate change policy.

Contributors: Valentina Bosetti, Carlo Carraro, Enrica De Cian, Thomas Longden, Emanuele Massetti, Lea Nicita, Fabio Sferra, Alessandra Sgobbi and Massimo Tavoni

June 18, 2014

Keynote at final meeting of Global-IQ Project

The keynote presentation that I gave at the final meeting of the EU FP7 project Global-IQ is available here.

The objective of the GLOBAL IQ project was three-fold:
  • to provide significant advances in the estimation of socio-economic impacts of global challenges – at Global, European and regional scale;
  • to identify optimal adaptation strategies;
  • to evaluate total costs and the optimal mix of adaptation and mitigation against global changes.

Led by Toulouse School of Economics, the Global IQ project involved eleven partners located in eight EU members states:

  • Toulouse School of Economics (TSE) France
  • Fondazione Eni Enrico Mattei (FEEM) Italy
  • Internationales Institut für Angewandte Systemanalyse (IIASA) Austria
  • Potsdam-Institut für Klimafolgenforschung (PIK) Germany
  • University of Gothenburg (UGOT) Sweden
  • Charles University in Prague (CUNI) Czech Republic
  • Istituto di Studi per l’Integrazione dei Sistemi (ISIS) Italy
  • London School of Economic (LSE) UK
  • Graduate Institute of International Studies in Geneva (HEID) Switzerland
  • Centre for Economic Policy Research (CEPR) UK