Second-Best Carbon Policy: Quantitative Evidence
Faculty Supervisor(s):

Samuel Kortum and Nick Ryan

Project Description:

Carbon emissions are a global externality. Harms depend on the total amount of CO2, without regard to its source. Imposing a harmonized global carbon price is an optimal response. Instead, countries are going their own way, with most not putting a price on carbon at all. How should a carbon policy be designed, by a country or coalition of countries, in such a second-best setting? We take two approaches to answer this question.

The first approach in this project is to consider what a planner would choose if it can’t dictate economic outcomes outside the region designing the policy. The solution to that problem suggests a carbon tax that, among other features, splits the tax burden between fossil fuel extractors and the users of energy. The value of that split, whether more on the supply or demand side, depends crucially on energy supply responses in other countries. The current project will build a quantitative version of this stylized planner’s problem, incorporating many countries with energy sources (including renewables) differing by carbon intensity. Using data on the distribution of extraction costs from each source in each country, the aim is to estimate the key elasticities. These values can be incorporated into the model to compute the optimal carbon tax and, in particular, the optimal split between supply and demand-side carbon taxes.

Without a globally harmonized carbon price the cheapest means to reduce global emissions may be in a poor country that chooses to put no price on carbon. It costs less to build renewable energy to displace construction of a new coal-fired power plant in a developing country than to displace a plant already operating in a developed country. Furthermore, developing countries justly object to using less energy, or more expensive energy, to compensate for the historical emissions of industrialized countries. Both efficiency and equity point to using transfer payments from developed countries to support green growth in developing countries. The second approach in this project is to study how well the Clean Development Mechanism (CDM), the largest such transfer program in the world, has actually worked. Have such offset programs reduced emissions below what would have occurred without them? Project proponents, who have more information than the certifiers of such projects, may claim (and be paid for) large emissions reductions that never happen. By assembling a new database on financial projections for CDM projects, together with firm-level data for the manufacturing sector in India and China, the goal is to test whether the claimed emissions reductions took place.

Requisite Skills and Qualifications:

Desired skills and qualifications fall into several categories.
1. Comfort with mathematical models, and with basic programming in a language such as Matlab.
2. Experience scraping data with python. Proficient python use is a strong plus, especially experience in the use of regular expressions for scraping semi-structured data.
3. Experience working with large manufacturing or household data sets in Stata / R. We will use the Indian Annual Survey of Industries (ASI) and China’s Annual Survey of Industrial Firms (ASIF).
4. Knowledge of Chinese. China hosts the largest number of CDM projects in the world. Knowledge of written Chinese is a plus to understand and interpret ASIF survey documentation.

Successful applicants will fulfill at least two of (1.), (2.) and (3.), and knowledge of all three of them is a strong plus.

Special Application Instructions: