Impacts of carbon trading scheme on air pollutant emissions in Guangdong Province of China

doi:10.1016/j.esd.2015.06.001

Energy for Sustainable Development

Volume 27, August 2015, Pages 174-185

https://doi.org/10.1016/j.esd.2015.06.001 Get rights and content

Highlights

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We assessed air pollutant emissions embedded in carbon emission trading scheme (ETS) among four sectors in Guangdong China
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Carbon ETS has significant co-benefits of reducing SO₂ and NO_x emissions.
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ETS could reduce the economic costs of achieving energy saving and CO₂ emission target
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Emission trading could reduce the economic costs of achieving energy saving and CO₂ emission target

Abstract

This study aims to assess the impacts of carbon emission trading scheme (ETS) policy on air pollutant emission reduction in Guangdong (GD) Province, especially with respect to the embedded air pollutant emission flow caused by carbon ETS. A Computable General Equilibrium (CGE) model is constructed to project the local emission trajectory of CO₂ and air pollutants under business as-usual (BaU) and policy scenarios in GD province and the rest of China from 2007 to 2020. To achieve the energy and carbon intensity targets, the carbon constraint and ETS policy are employed to promote energy saving and CO₂ emission reduction. The simulation results show that the carbon ETS has the co-benefits of reducing SO₂ and NO_x emissions by 12.4% and 11.7% in 2020 compared with the BaU scenario. Along with the carbon trading volume of 633 million tons created by the ETS scenario, an embedded amount of 38,000 tons of air pollutants is exchanged among carbon trading sectors, which valued about 50 million USD. Although the current carbon and air pollutant emission markets are independent from each other, the evaluation of the co-benefits needs to be considered further in the policy making process.

Introduction

As a result of the rapid development of China's economy, energy consumption increases drastically and various harmful substances from energy consumption are discharged to the atmospheric environment causing serious air pollution. Especially, due to China's coal dominated energy structure, acid rain and smog have seriously polluted the environment. Furthermore, as the ownership of China's automobile has greatly increased in recent years, nitrogen oxide (NO_x) and particle matter (PM) have become the main air pollutants in an urban area, causing adverse effects on the health of urban residents.

Although during the “11th five-year” (FYP during 2006–2010) period the energy intensity in GD decreased by 16.4%, the conventional growth mode of industrialization and urbanization results in rising energy demand and CO₂ emission as well as environmental degradation. As one of the most important economic provinces of China, Guangdong (GD) consumes 4.6% of China's coal, 21.9% of crude oil, 12.8% of natural gas and 10.6% of electricity in 2012 (Table 1). Consequently, air pollution in Guangdong and the Pearl River District has deteriorated in recent years.

In order to prevent further deterioration of air quality and protect human health and the ecosystem, the Chinese government has implemented a series of national control policies to reduce the emissions of air pollutants since 2005 (Wang and Hao, 2012). The 11th FYP for national environmental protection required the reduction of annual emissions of sulfur dioxide (SO₂) in 2010 by 10% from its 2005 level. Furthermore, in China's 12th FYP (2011–2015), nationwide controls of NO_x emission will be implemented along with the controls of SO₂ and primary particles. The Ministry of Environmental Protection (MEP) of China has set a target to reduce the national NO_x emissions in 2015 by 10% from the 2010 level.

At the regional level, the Guangdong provincial government has promulgated a serial policy in thermal power plant nitrogen, volatile organics, motor vehicle pollutant emission, industrial boiler pollution and cement industry, which all emphasizes on establishing the work progress report, supervising the notification mechanism, and strengthening the monitoring capacity-building (People's Government of Guangdong Province, no. 6, 2014). Furthermore, Guangdong Province also introduced a “comprehensive energy saving and reduction program” to achieve the main target of energy saving and air pollutant by 2015, which requires the energy consumption of per GDP to decrease by 18% and 31% in 2010 and 2005, respectively; chemical oxygen demand (COD) and sulfur dioxide (SO₂) emissions to reduce by 12% and 14.8% compared with 2010, respectively; and ammonia and nitrogen oxide (NH₃ and NO_x) emissions to decrease by 13.3% and 16.9% in 2010, respectively (People's Government of Guangdong Province, no. 14, 2012). Guangdong was also selected as a low-carbon pilot province designated as one of the 13 pilot low-carbon zones in China by the National Development and Reform Commission (NDRC) in 2010, with a tough target of reducing carbon intensity of GDP by 19.5% and at least 45% from 2005 level in 2015 and 2020, respectively. In addition, some pilot energy and climate polices at the regional level are implemented and assessing such policies with complex system models have attracted attention in China. For instance, Guangdong has been selected as a pilot to conduct the carbon emission trading scheme (ETS) in 2013, and the air pollutant trading market has started in 2014 in this province. Evaluation of the effectiveness of carbon mitigation policies is vital for future policy design at both national and regional levels.

Section snippets

Literature review

So far there is a growing awareness that sustainable development requires an integrated and system-level redesign of the entire socio-ecological regime to coordinate different management policies. Some researchers discussed the concept of co-benefits in the air pollution control and counter climate change (Kanad et al., 2013, Nemet et al., 2010, Jack and Kinney, 2010). If well established, the evaluation of such dual or multiple benefits or profit schemes could provide strong incentives for the

The CGE model

This study uses a dynamic CGE model jointly developed by Guangzhou Institute of Energy Conversion (GIEC) China and National Institute for Environmental Studies (NIES) Japan. The model is a two-region recursive dynamic CGE model that includes Guangdong (GD) Province and the rest of China (ROC). The technical description of the static module is provided in the paper (Wang et al., 2015).

The major model features are similar to the one-region dynamic version (Dai et al., 2012). It includes a

Total CO₂, SO₂ and NO_x emission trends

The model simulation results show that the total CO₂ emission will keep increasing 1.5 times from 2010 to 2020 in the BaU scenario and CO₂ emissions of Guangdong share in China will decrease from 8% in 2010 to around 7% in 2020, reflecting the trend that China's CO₂ emissions increase 1.6 times over the same period (Fig. 2).

On the other hand, under the BaU scenario, the SO₂ and NO_x emissions in 2020 will be 0.76 and 0.79 times of that from 2010. Then the SO₂ and NO_x of Guangdong Province in

Conclusions

This study estimates the impacts of carbon emission trading scheme (ETS) policy on air pollutant emission reduction in Guangdong (GD) Province, especially with respect to the embedded air pollutant emission flow caused by carbon ETS. A Computable General Equilibrium (CGE) model is constructed to project the local emission trajectory of CO₂ and air pollutants under business as-usual (BaU) and policy scenarios in GD province and the rest of China from 2007 to 2020. To achieve the energy and

Acknowledgments

This work was co-funded by the Strategic Program Fund (SPF) project (no. PPY_CHN 1128-CTM3) and the Chinese Academy of Sciences Director of the Innovation Fund Project (no. y407pc1001) and the “Global Environmental Research Fund” 2-1402 of the Ministry of the Environment of Japan.

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Impacts of carbon trading scheme on air pollutant emissions in Guangdong Province of China

Highlights

Abstract

Introduction

Section snippets

Literature review

The CGE model

Total CO2, SO2 and NOx emission trends

Conclusions

Acknowledgments

Energy Policy

Energy Econ

Sci Total Environ

Applied Energy

Energy Policy

Energy Policy

Appl Energy

J Clean Prod

Sci Total Environ

Energy

Curr Opin Environ Sustain

J Clean Prod

Energy

Energy Policy

Environ Sci Pol

J Environ Sci

Energy Policy

Energy

J Clean Prod

Total CO₂, SO₂ and NO_x emission trends