Ho, Mun S.

Based on econometric estimation using data from the Chinese Urban Household Survey, we develop a preferred forecast range of 85–143 percent growth in residential per capita electricity demand over 2009–2025. Our analysis suggests that per capita income growth drives a 43% increase, with the remainder due to an unexplained time trend. Roughly one-third of the income-driven demand comes from increases in the stock of specific major appliances, particularly AC units. The other two-thirds comes from non-specific sources of income-driven growth and is based on an estimated income elasticity that falls from 0.28 to 0.11 as income rises. While the stock of refrigerators is not projected to increase, we find that they contribute nearly 20 percent of household electricity demand. Alternative plausible time trend assumptions are responsible for the wide range of 85–143 percent. Meanwhile we estimate a price elasticity of demand of −0.7. These estimates point to carbon pricing and appliance efficiency policies that could substantially reduce demand.

China has set a goal of reducing its CO₂ intensity of GDP by 60–65% from the 2005 level in 2030 as its nationally determined contribution (NDC) under the Paris Climate Change Agreement. While the government is considering series of market and nonmarket measures to achieve its target, this study assesses the economic consequences if the target were to meet through a market mechanism, carbon tax. We used a dynamic computable general equilibrium model of China for the analysis. The study shows that the level of carbon tax to achieve the NDC target would be different depending on its design features. An increasing carbon tax that starts at a small rate in 2015 and rises to a level to meet the NDC target in 2030 would cause smaller GDP loss than the carbon tax with a constant rate would do. The GDP loss due to the carbon tax would be smaller when the tax revenue is utilized to cut existing distortionary taxes than when it is transferred to households as a lump-sum rebate.

An environmental fiscal reform (EFR) represents a transition of a taxation system toward one based in environmental taxation, rather than on taxation of capital, labor, or consumption. It differs from an environmental tax reform (ETR) in that an EFR also includes a reform of subsidies which counteract environmental policy. This research details different ways in which an EFR is not only possible but also a good option that provides economic and environmental benefits. We have developed a detailed dynamic CGE model examining 101 industries and commodities in Spain, with an energy and an environmental extension comprising 31 pollutant emissions, in order to simulate the economic and environmental effects of an EFR. The reform focuses on 39 industries related to the energy, water, transport and waste sectors. We simulate an increase in taxes and a reduction on subsidies for these industries and at the same time we use new revenues to reduce labor, capital and consumption taxes. All revenue recycling options provide both economic and environmental benefits, suggesting that the “double dividend” hypothesis can be achieved. After three to four years after implementing an EFR, GDP is higher than the base case, hydrocarbons consumption declines and all analyzed pollutants show a reduction.

Understanding the rapidly rising demand for energy in China is essential to efforts to reduce the country's energy use and environmental damage. In response to rising incomes and changing prices and demographics, household use of various fuels, electricity and gasoline has changed dramatically in China. In this paper, we estimate both income and price elasticities for various energy types using Chinese urban household micro-data collected by National bureau of Statistics, by applying a two-stage budgeting AIDS model. We find that total energy is price and income inelastic for all income groups after accounting for demographic and regional effects. Our estimated electricity price elasticity ranges from - 0.49 to -0.57, gas price elasticity ranges from -0.46 to -0.94, and gasoline price elasticity ranges from -0.85 to -0.94. Income elasticity for various energy types range from 0.57 to 0.94. Demand for coal is most price and income elastic among the poor, whereas gasoline demand is elastic for the rich.