Study on the distribution of PM emission rights in various provinces of China based on a new efficiency and equity two-objective DEA model

doi:10.1016/j.ecolecon.2021.106956

Ecological Economics

Volume 183, May 2021, 106956

https://doi.org/10.1016/j.ecolecon.2021.106956 Get rights and content

Abstract

Under the premise that the total emissions of PM(particulate matter) of air pollution is established, the allocation of PMemission rights in various provinces of China is the feasible way to governance the ash haze. In this paper, a data envelopment analysis (DEA) model that takes into account the objectives of efficiency and equity is constructed. On the basis of evaluating the PMemission performance of various provinces in China, the corresponding allocation steps are put forward. Then, the results of theDEAmodel and information entropy are compared. The research shows that: (1) under the same constraint conditions, the emission right of PMis allocated with DEA model, which leads to lower cost, less fluctuation of distribution amount and higher overall national performance. (2) the populationGinicoefficient method is used to evaluate and it is found that the Gini coefficient value is smaller according to the distribution results of DEA model, indicating that the PM emission right amount is more fairly distributed among provinces. The functions of this paper are as follows: (1) a new double-objective DEA model is constructed, which is suitable for multi-agent performance evaluation with multiple objectives; (2) a scheme to allocate PM among provinces is proposed, which can provide reference for the practice of haze reduction in China

Introduction

With the continuous development of global industrialization, many countries in the world have experienced air pollution problems. Historically, the Mas valley smog in the 1930s, the London smog in 1952 and the photochemical smog in Los Angeles in 1943 have all brought serious impacts on the local economy and environment (Nemery et al., 2001; Lucier, 2003; SCAQMD, 2003). In recent years, air pollution has become particularly serious in some developing countries. In mid-November 2016, the average concentration of fine particulate matter (PM) in the air in Tehran, Iran exceeded 150 micrograms per cubic meter, resulting in more than 400 deaths (Xinhua News Agency, 2016a). According to the report of Global Burden of Disease (GBD), it is estimated that about 2 million people in India died prematurely due to air pollution in 2016 (GBD 2015 Mortality and Causes of Death Collaborators, 2016).

In the past 20 years, air pollution in China, with PMas a typical representative, has been increasing continuously. 2013 was the worst year for haze pollution in 52 years, according to China's state of the environment bulletin released in 2013. In the winter of 2016, a large area of haze appeared in the central and eastern regions. According to remote sensing monitoring data, the haze area once reached 1.88 million square kilometers (Xinhua News Agency, 2016b). At the beginning of 2018, the Beijing-Tianjin-Hebei region and surrounding areas experienced heavy regional air pollution for nearly 10 days, and more than 60 cities implemented regional emergency linkage measures (Xinhua News Agency, 2018). During the ‘two sessions’ in 2019, moderate and heavy pollution weather continues to occur in Beijing and other places (The Environmental Protection Agency, 2019), and the PM_2.5concentration in some areas even exceeds 300 μg/m³. Air pollution interferes with daily traffic, reduces inbound tourism and endangers health, which has aroused great concern from the whole society (Wang et al., 2018a; Ni et al., 2019). How to effectively control air pollution has become an important issue for the government, academia and the public.

From the experience at home and abroad, there are two major types of air pollutants governance, one is the source control (Rosa et al., 2019). For example, on the basis of analyzing the composition of air pollutants, sources of pollution are found, and then methods such as shutdown, production limit and technical transformation are adopted to reduce the emission of pollutants from the source. The difficulty lies in how to analyze the components and trace the pollution sources. The second category is socio-economic means. Such as the implementation of emission permit system, levy taxes, carry out emission rights trading. In the second approach, when the total amount of pollution is set, the key lies in how to allocate the emission to each emitter.

According to a research report released in November of 2012 (Zhang and Crooks, 2012), less than 1% of the 500 largest cities in China had achieved the air quality standards recommended by the World Health Organization. In addition, China accounts for seven out of the ten most polluted cities in the world. To address such important public goods as air pollutants, there are two types of regulations: one is tax regulation (Baumol and Oates, 1988), and the other is emission right trading (Nordhaus, 2005). Emissions right trading regulations work by first setting an environmental goal at a national, or sometimes regional/local level, imposing a limit on the overall amount of pollution that sources are allowed to emit into the environment. This environmental goal (limit) is a critical part of an emissions trading program. For instance, in the international arena, carbon emission right regulations intend to control the total emission amount of carbon by allocating the initial emission rights at the local level (country, region, province), and trade surplus emission rights on the market. The key question in this type of regulation is what will the initial carbon right (limit) be for each province respectively? Emission rights of economic entities can be settled in the form of emission permits, and surplus emission rights can also be traded. In this context, the method of stipulating emission rights in the form of permits is known as the initial allocation of emission rights (Burton and Sanjour, 1969, Burton and Sanjour, 1970).

Then the allocation of air pollutant emission rights is a key component in emission trading programs. The environmental governance practices of China do not consider or define how the allocation of air pollutant emission rights should be established for each of the provinces. The Article 21 of the Air Pollution Prevention and Control Law of the People's Republic of China (revised in 2018) stipulates that the specific methods for determining the total volume control objectives and the corresponding control indicators will be formulated by the ecological environment department of the State Council in conjunction with the relevant departments.

However, as far as we know, the question of how to determine the emission rights for each province (municipality) and what the goals will be for them remains unclear. Based on the above considerations, this paper contributes to solving the emission rights problem by proposing a bi- objective DEA model to assign initial pollutant emission rights to various regions of the country. Undoubtedly, this research contributes in an innovative way to the implementation and improvement of the pollutant emission rights program in China.

To assign initial pollutant emission rights to various regions of the country under a total emissions target, this paper proposes a DEA model from two perspectives of fairness and efficiency. Then, takingPMas an example, the emission right of each province is allocated on the basis of evaluating the emission performance of each province. To verify the rationality of the methods proposed in this work, the results obtained are compared with the distribution results based on the entropy of the information. The empirical conclusions can provide new ideas and basis for the practical work of reducing PMemissions in China.

Section snippets

PMemission trading research

Air pollutants include primary pollutants (such as sulfur dioxide, nitrogen dioxide, carbon monoxide, particulate matter, etc.) and secondary pollutants (such as sulfate aerosols, nitrate aerosols, ozone, photochemical oxidants, and active intermediates, etc.). Among them, PMis a typical representative of air pollution, including both primary and secondary pollutants (Fu et al., 2016).

Scholars believe that an important prerequisite for launchingPMemission trading is to determine the emission of

DEA model with consideration of efficiency and equity

First of all, the economic output efficiency should be guaranteed first. The objective function is to maximize the overall average efficiency value, that is, the larger the ratio of output to input, the better, or at least the ratio should not be reduced. Secondly, the principle of per capita equity should be reflected as much as possible, that is, the proportion of PM distribution in the total PM emission of the whole country should be as close as possible to the proportion of the population

Index selection

The selection of input and output indicators should have a clear theoretical basis and economic meaning. In the generation of PM and the evaluation of governance efficiency, capital and labor input are essential. The input side of the traditional production function includes capital and labor, while the output side is GDP. Later, scholars added energy, environment, climate and other factors affecting GDP output on the input side (Wang et al., 2018b). This paper also uses this idea for reference

Original data

The raw and standardized data of GDP, capital input, energy consumption (coal, oil and natural gas), labor force and PM_2.5 emission right of each province in 2016 are shown in Table 3:

Weight assignment

The information entropy method needs to assign a value to the weight. According to formula (6)–(9), the information entropy of each indicator is calculated to determine the relative importance of the indicator. On the basis of standardized processing of the data in Table 3, the entropy weight of each indicator (Wj

Summary and conclusions

The problem of allocating air pollutant emission rights that simultaneously incorporates equity and efficiency dimensions is a new and challenging problem. First, the distribution of air pollutant emission rights is related to the characteristics of air pollutants. The generation, combination, and transmission of air pollutants have a great relationship with the resources and environment of the places where air pollutants are generated. Therefore, scholars mostly start from the production and

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

The National Social Science Foundation of China (17BGL142, 18ZDA052); The National Natural Science Foundation of China (91546117, 71904117). The research of Prof. Ernesto DR Santibanez Gonzalez has been partially supported by ANID – Chile, grant Fondecyt No. 1190559.

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Methodological and Ideological OptionsStudy on the distribution of PM emission rights in various provinces of China based on a new efficiency and equity two-objective DEA model

Abstract

Introduction

Section snippets

PMemission trading research

DEA model with consideration of efficiency and equity

Index selection

Original data

Weight assignment

Summary and conclusions

Declaration of Competing Interest

Acknowledgements

Socio Econ. Plan. Sci.

Ecol. Econ.

Ecol. Econ.

Lancet

J. Clean. Prod.

Comput. Ind. Eng.

Ecol. Econ.

Atmosphere

Ecol. Indic.

Cleaner Prod.

Omega.

Ecol. Econ.

The Theory of Environmental Policy

Pollutant emission quota allocation based on DEA

O. R. Transactions.

Multi-criteria decision making and fairness evaluation of water ecological carrying capacity for inter-regional green development

Environ. Sci. Pollut. Res.

Better resource utilization: a new DEA bi-objective resource reallocation approach considering environmental efficiency improvement

Comput. Ind. Eng.

Better resource utilization: a new DEA bi-objective resource reallocation approach considering environmental efficiency improvement

Comput. Ind. Eng.

Final Guidance for Incorporating Environmental Justice Concerns in EPA's NEPA Compliance Analyses

Market-based emissions regulation when damages vary across sources: what are the gains from differentiation

J. Journal of the Association of Environmental and Resource Economists

Impacts of historical climate and land cover changes on fine particulate matter (PM2.5) air quality in East Asia between 1980 and 2010

Atmos. Chem. Phys.

Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980-2015: a systematic analysis for the global burden of disease study 2015

Lancet

Tradeable CO2 emission permits: initial distribution as a justice problem

Environmental values

The optimal distribution of pollution rights in the presence of political distortions

Environ. Resour. Econ.

The contribution of outdoor air pollution sources to premature mortality on a global scale

Nature

Quantifying the efficiency and equity implications of power plant air pollution control strategies in the United States. Environ health Perspect

May

Regional difference decomposition analysis of sulfur dioxide emission intensity in China

Chin. Popu. Res. Envir.

Methodological and Ideological Options
Study on the distribution of PM emission rights in various provinces of China based on a new efficiency and equity two-objective DEA model

Impacts of historical climate and land cover changes on fine particulate matter (PM_2.5) air quality in East Asia between 1980 and 2010

Tradeable CO₂ emission permits: initial distribution as a justice problem