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Carbon geography: The political economy of congressional support for legislation intended to mitigate greenhouse gas production

What influences climate change policy? This column shows that a congressional district’s per capita carbon emissions and conservative ideology lower the probability that a representative votes in favour of a pro-environment bill, while county per capita income increases it.

Faced with ongoing world population growth and per capita income growth, the world’s greenhouse gas emissions could double over the next 50 years. Scientists and economists are calling for a sharp reduction in greenhouse gas emissions (e.g. Heal, 2008, Stiglitz and Stern 2009). The Obama Administration is pursuing more stringent climate change mitigation legislation. But what determines climate policy?

In recent research (Cragg and Kahn 2009), we examine the geography of carbon emissions across the US and argue that several basic facts are relevant for understanding the incidence of carbon regulation. Certain “high-cost” geographical areas are likely to be hotbeds of political opposition to climate change regulation. As defined by the basic theory of pressure groups, groups that face both high potential costs of regulation and low transaction costs of organising will lobby their congressional representatives to block or mitigate such regulation even if this regulation is globally efficiency-enhancing.

Using a county-level dataset based on 2002 carbon emissions, we analyse the geographical distribution of carbon emissions relative to the political leanings and demographic characteristics of the voters in each area. We document that counties with high per capita carbon emissions are more likely to be poorer and represented by a conservative (based on voting records). Assuming that counties with high carbon emissions will have the most trouble substituting away from carbon, these areas will more heavily bear the cost of carbon regulation. Conservative, poor, rural areas will face a higher carbon bill under a cap and trade system than liberal, rich, urban areas. This compounds the regressively of any energy tax or cost increase, making some offset a political necessity.

Geographical carbon data

We use the 2002 Vulcan carbon emissions data set (Gurney et. al. 2005). Carbon (not carbon dioxide) is measured in tons per capita. The data are based on production, not consumption.1 Figure 1 presents the geography of per capita carbon production. The coastal states, such as California, Oregon, and Washington, stand out as low-carbon areas largely due to their proximity to large Northwest hydroelectric facilities and natural gas. In contrast, the Midwest and South have higher than average per capita emissions largely due to their coal intensive electric generation.

Figure 1. Carbon emissions per capita

County carbon regressions

Our research shows that measures of political ideology and household income can explain the county-level per capita carbon emissions. Specifically, countries with conservative ideology are associated with higher emissions. Major liberal cities such as Portland, San Francisco, and Boston are not hotbeds of manufacturing activity or coal-fired electric power generation and thus will face a lower total carbon bill for complying with new climate legislation. Such areas have already taken steps to “decarbonise” and are naturally lower in carbon due to geographic advantage.

Another way to get a handle on the political economy factors is to look at Congressional voting patterns. The theory is a simple cost-benefit approach. – Liberal representatives gain greater benefits from voting for climate change legislation, as they may personally favour such regulation and will recognise that their constituents will also support such legislation. On the cost side, we focus on differences in per capita carbon emissions across counties and congressional districts. If a geographical area features higher per capita carbon production, then we assume that this area would face a higher cost from enacting carbon legislation. Consumers of carbon-intensive goods and owners of assets whose value is derived from fossil fuels (i.e. shareholders of coal power plants) will bear part of the incidence of carbon regulation.

We also examine whether combating climate change is a normal good by testing whether richer areas are more likely to support climate legislation. This simple model predicts that geographical areas featuring conservative leaders of poor, rural areas that are carbon-intensive are the least likely to support climate change mitigation regulation.

To test for the role of per capita income, per capita carbon, and ideology in explaining carbon mitigation voting patterns, we use recent voting data from the 110th Congress. In the 110th Congress, representatives voted on various bills that had direct implications for mitigating climate change. Relying on the 2007 League of Conservation Voter Scorecard to help us identify key carbon legislation, we examine House Roll Call 555 (motion to strike non-binding resolution endorsing mandatory limits on global warming pollution), House Roll Call 827 (renewable energy requirements for utilities), and House Roll Call 835 (the Renewable Energy and Energy Conservation Tax Act of 2007).

We examine whether representatives’ votes were a function of the District’s per capita carbon, per capita income and the representative’s ideology.

As seen in Table 1, conservative representatives from poor, carbon intensive districts are the least likely to vote in favour of environmentally “friendly” energy legislation in the 110th Congress. Consider the regression results for Bill 827. A doubling of a congressional district’s carbon emissions per capita lowers the probability that a representative votes in favour of this bill by 17 percentage points. Doubling this district’s per capita income increases the probability that a representative votes in favour of this bill by 62 percentage points. Increasing the ideology measure by one standard deviation (more conservative) reduces the probability that a representative votes in favour by 59 percentage points. In the fourth column of Table 1, we average the voting across the three bills and estimate a linear probability model. The results are quite similar to the previous results.

Table 1. Congressional voting on anti-carbon legislation in the 110th congress

  (1) (2) (3) (4)
  Bill 555 Bill 827 Bill 835 Average
Across the
Three Bills
(carbon per capita)




(District income per capita)
Congressman ideology score -.111
Obs. prob. .638 .534 .541  
pred. prob. .995 .572 .602  
Observations 420 406 408 391
Chi2 427.93 313.84 470.91  
prob>chi2 0 0 0  
R-squared       .828

Columns (1)-(3) report estimated marginal changes in probabilities based on stata’s “dprobit” command. The standard deviation for congressman ideology score equals .51. The dependent variable in column (4) is based on the share of the “pro-environment” votes the representative cast on the three bills listed in columns (1-3). Standard errors are presented in brackets. Coefficient estimate that are statistically significant at the 10% level are indicated by a “*”, at the 5% level by “**”, and at the 1% level by “***”.


By combining data on county per capita carbon emissions, county demographic data, and congressional voting data, we have uncovered several facts that will likely play an important role in constraining climate legislation. Self-interested politicians who represent conservative voters, poor communities, and districts whose per capita emissions are high are least likely to vote in favour of carbon mitigation regulation. Economists and policy makers will need to address these constraints if they want to help design climate legislation that will have a meaningful effect on controlling carbon emissions and be passed by members of congress.

Note: The views expressed in this paper are strictly those of the authors and do not necessarily state or reflect the views of The Brattle Group, Inc, its clients, or UCLA.


1 An alternative approach for studying the geography of carbon dioxide emissions is presented in Glaeser and Kahn (2008). They estimate residential household production of carbon dioxide for a standardised household as a function of how much gasoline, electricity and the resulting greenhouse gas emissions from producing this power and home heating a household would consume in each of 63 major cities.


Glaeser, Edward and Matthew E. Kahn, “The Greenness of Cities: Carbon Dioxide Emissions and Urban Development.” NBER Working Paper 14238, 2008.

Gurney, Kevin R., Yu-Han Chen, Takashi Maki, S. Randy Kawa, Arlyn Andrews, and Zhengxin Zhu, “Sensitivity of Atmospheric CO2 Inversions to Seasonal and Interannual Variations in Fossil Fuel Emissions.” Journal of Geophysical Research, 110, D10308, 2005.

Heal, Geoffrey (2008). “Climate economics,” VoxEU.org, 9 June 2008.

Michael I. Cragg & Matthew E. Kahn, 2009."Carbon Geography: The Political Economy of Congressional Support for Legislation Intended to Mitigate Greenhouse Gas Production," NBER Working Papers 14963.

Poole, Keith T. and Howard Poole, “Congress. A Political-Economic History of Roll Call Voting,” Oxford University Press, 1997. (see http://voteview.com/dwnomin.htm).

Stiglitz, J. and N. Stern (2009). “Climate crisis and economic crisis,” Commentary on Vox’s Global Crisis Debate, 7 March 2009.

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