The impact of emission trading scheme and the ratio of free quota: A dynamic recursive CGE model in China
Introduction
China, as the largest primary energy consumption country, accounts for 23% of the total fossil energy consumption in the world in 2014 [1]. Coal remains the largest proportion in China’s energy structure. China’s total energy consumption in 2014 was 4260 million tons of coal equivalent (Mtce) [2]. Coal, oil, and natural gas accounted for 66.0%, 17.1% and 5.7% of the total energy consumption in China respectively, while hydropower and nuclear power etc. accounted for 9.8% [3] and the corresponding problems of carbon dioxide emissions—environmental pollution—have become an increasingly acute issue [4]. China, as one of the largest economies in the world, is the biggest emitter worthy of the name, and the amount of annual growth now has more than the sum of North America and Europe. How to take the responsibility of developing countries, and to better deal with the problem of global climate change, has become one of the most important issues in China.
The publication of U.S.–China Joint Announcement on Climate Change [5] and Enhanced actions on climate change: China’s intended nationally determined contributions [6], reflect China’s determination and efforts to reduce CO2 emissions. The common methods of emission mitigation are carbon tax [7], carbon emission trading [8], [9], carbon sink [10], Certified Emission Reductions (CER) [11], renewable energy [12] or carbon capture and storage [13], [14], [15], [16]. National Development and Reform Commission (NDRC) is expected that, in 2017, the national carbon trading market will be officially launched, and China’s 7 carbon emissions trading pilot cities have been running for 4 years [17], [18]. But which trading mode is the most efficient in protecting China’s economy, and reducing carbon emissions, is an issue worth pondering.
Since 2005, the developed countries have launched or implemented the carbon emissions trading scheme (ETS) [19], and have played a good effect in promoting the emission reduction, i.e. UK Emissions Trading Group (ETG), European Union Greenhouse Gas Emission Trading Scheme, (EU-ETS), Chicago Climate Exchange (CCX), National Trust of Australia (NSW). EU-ETS, which is the international carbon emissions exchange, exists some structural defects like over quota in I period and II period, nevertheless, greenhouse gas emissions in EU decreased by 12% though the efforts, which is an important example for China’s future carbon trading market.
According to the National Development and Reform Commission’s design, the first batch of industry in the national carbon trading market will be made of 5 traditional manufacturing (steel, nonferrous metals, cement, chemical and electricity) and transportation industry, which is very close to EU-ETS at period III: the covering industry in EU-ETS period I is electricity, steel, refined oil, cement and glass, then, the air transportation is added to cover in period II and chemicals is added in period III. China’s carbon emissions trading market in the future may involve 3–4 billion tons of CO2, or about 50% of carbon emissions in 2010. Trading pattern is a combination of public auction and free distribution, which is the same as the 7 pilot regions (Beijing, Tianjin, Shanghai, Chongqing, Guangdong, Hubei and Shenzhen) in China, however, compared to EU-ETS, the proportion of free quota for the covering industry is relatively large: the ratio of free quota to paid quota is 97:3 [20].
International experts have carried out a lot of analyses on the policy and mechanism of reducing emissions. Alton et al. [21] discusses introducing carbon taxes in the South Africa. The economic impact of different carbon tax revenue recycling schemes is studied by Liu et al. [22]. Cummins [23] analyzed interactions of European Union Allowance (EUA) and Certified Emissions Reduction (CER) with the method of econometrics. Chatzizacharia et al. [24] put forward a plan to meet the energy needs of the state of Greece in conjunction with assumed responsibilities to confront global warming by using time series models to predict the energy needs and CO2 emissions for the next 25 years. Fais et al. [25] evaluates the critical contribution of the industry sector to long-term decarbonisation, efficiency and renewable energy policy targets in UK. As an effective means of reducing emissions, ETS is a focus of international experts. Zhang et al. [26] helped to recognize the features of the EU ETS and its effect on others. Kara et al. [27] focused on the impacts of EU CO2 emissions trading on electricity markets. Crossland et al. [28] carried out the research on the performance of emissions trading scheme in EU. Chang et al. [29] proposes the allocation of CO2 emissions increment quotas and carbon intensity reduction burdens based on information entropy method. Richstein et al. [30] finds two ways of policy implementations can successful restore carbon prices in EU-ETS. Hong et al. [31] aims to develop a decision support model for establishing benchmarks as a tool for free allocation in the construction industry, and a total of 60 possible combinations were evaluated in terms of the economic and environmental impact. Mo et al. [32] accesses the impact of Chinese carbon emission trading scheme on low carbon energy investment and show a result that other policy measures will be needed to promote low-carbon energy development in China.
In recent years, as a popular policy simulation tool, computable general equilibrium (CGE) model has been widely employed in the analysis of tax, public consumption, tariff and climate policy [33], [34], [35], [36], [37], [38]. Dai et al. [39] assesses the economic impacts and environmental co-benefits of large-scale development of renewable energy in China toward 2050 using a dynamic computable general equilibrium (CGE) model with distinguished improvements in the power sector. Pollution is usually expressed by endogenous variable in the production function or utility function of CGE model to evaluate the energy, environment and economic impact of the public policy and international trade policy [40], [41], [42].
However, the economic and environmental impacts of different carbon allocation schemes are little studied, not to mention the impact of free allocation of carbon credit on carbon trading prices. The question this paper seeks to answer, then, is to find out the best carbon emission right distribution pattern for China to choose and the reasonable trading prices in different patterns. In this paper, we will establish 10 scenarios to simulate different free quota ratio of carbon trading scheme at the time when national ETS is started, to analyze the impact of different rates on carbon trading price, CO2 emissions, economic output and emission reduction costs. Finally, by using a dynamic recursive CGE model, we found the relationship between free quota rate and carbon price under the same emission reduction targets—which is main contribution in this paper, and the impact of ETS.
Section 2 explains the CGE model and the dataset used in this paper. In Section 3 we describe 10 counter-measured (CM) scenarios based on experiences of different pilot cities. Section 4 shows the simulation results and discussion. The final conclusions are obtained in Section 5.
Section snippets
CGE model
Computable General Equilibrium model is deeply rooted in the standard microeconomic theory. CGE model builds a number of markets (product markets, factor markets and exchange market between domestic and foreign goods) and market players (residents, enterprises, government and foreigners) [43], [44]. Market equilibrium can be formed by the economic subject optimization. All CGE models constructed are based on traditional Walras paradigm, which means CGE models can be described as a system of
Scenarios of ETS
Based on the NDRC’s planning which this paper introduced above, the time span of the CGE model is set from 2017 to 2035: 2017 is the start time of ETS in China. According to Enhanced action on climate change: China’s intended nationally determined contributions, China intends to lower carbon dioxide emissions per unit of gross domestic product (GDP) by 60–65% from the 2005 level in 2030. In this paper, carbon intensity is an exogenous variable. To reach 65% target, CI will decrease exogenously
Carbon intensity
In this paper, we assume that the carbon emission intensity is an exogenous variable, with 3.8876% decreasing per year, so all the counter measured scenarios (CMXX) can achieve the goal of carbon intensity in 2030: to lower carbon dioxide emissions per unit of GDP by 65% from the 2005 level. Carbon emission intensity in BAU scenario and CMXX scenarios as shown in Fig. 3. In the BAU scenario, the carbon emission intensity decreases with the decreasing acceleration, and the carbon emission
Conclusions
This paper establishes 10 ETS scenarios of different free quota ratio of carbon emission rights, and uses a computable general equilibrium model to analyze the difference of carbon price and the impact on the GDP, energy consumption, sector output, emission reduction costs, market prices and the utility of residents under a certain reduction targets. In accordance with the NDRC, 2017 is the beginning year of carbon trading system, so this paper estimates from 2017 to 2035 and finally gets the
Conflicts of Interest
We confirm that we have given due consideration to the protection of intellectual property associated with this work and that there are no impediments to publication, including the timing of publication, with respect to intellectual property. In so doing we confirm that we have followed the regulations of our institutions concerning intellectual property.
Acknowledgments
This study is supported by the National Social Science Foundation of China (Grant No. 15BGL145), the National Natural Science Foundation of China (Grant No. 71471061), the Fundamental Research Funds for the Central Universities (No. 2015ZD33) and Philosophy and Social Science Research Base of Hebei Province.
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