With worldwide energy consumption in a constant increase, large amounts of Greenhouse Gasses (GHG), especially CO2
, are being emitted directly into the atmosphere. This has triggered a series of global climate change issues—such as the Greenhouse Effect—and is posing a considerable threat to human survival and development. In order to address these challenges, through the United Nations Framework Convention on Climate Change (UNFCCC) in 2010, the United Nations advised all parties to make urgent efforts to significantly reduce their global carbon emissions [1
]. Against such a background, countries all over the world are facing huge pressure to reduce emissions, especially developing countries, which need to balance the dual needs of environmental protection and economic growth. As the largest developing country, in the past few decades, China’s rapid economic development has caused steep increases in CO2
emissions, which mainly stem from fossil fuel combustion [2
]. In 2017, CO2
emissions from fossil fuel combustion in China reached up to 9.26 Gt, accounting for 28.19% of the global total emissions (IEA Statistics, 2018, IEA Energy Atlas, http://energyatlas.iea.org/#!/tellmap/1378539487
), which represents an over-fourfold increase from 2.12 Gt in 1990. As the country with the largest CO2
emissions in the world, China is facing tremendous pressure to reduce its emissions. In its Intended Nationality Determined Contributions (INDC) submitted to the United Nations Framework Convention on Climate Change (UNFCCC) on 30th June 2015, China promised that CO2
emissions per unit of GDP (CO2
/GDP) will be reduced by 60–65% by 2030, on a 2005 basis [3
]. In order to achieve national CEI reduction targets, it is crucial to delve into the influential factors and to clarify the driving mechanisms of CEI.
Since 2005, China has been committed to the simultaneous effort of economic development and environmental protection. Carbon intensity was reduced from 2.88 tons/10,000 yuan in 2005 to 1.11 tons/10,000 yuan in 2017. However, uneven regional development remains an urgent development problem. As shown in Figure 1
, the level of China’s economic development has been significantly differentiated between regions, and the regional distribution of CEI has been characterized as high in the north and low in the south. The gap between the maximum value of 13.254 tons/10,000 yuan and the minimum value of 0.513 tons/10,000 yuan is quite large. The huge differences urgently need to be improved through the coordinated regional development route in order to achieve overall low-carbon development. In 2019, Chinese President Xi Jinping proposed to further promote the regional coordinated development strategy [4
], which provides good strategic guidance and policy support for regionally-coordinated emission reduction. Therefore, studying the temporal and spatial characteristics of carbon intensity in China’s provinces, and the driving mechanism under the spatial effect, is of great significance for regional low-carbon cooperation in China. In addition, research on carbon intensity reduction from a provincial perspective in China can also provide a reference for emission reduction research in other countries or regions, especially in terms of regional cooperation in emission reduction.
China has a vast territory, a large population, complex national conditions, and great differences in its economy, industrial structure, energy consumption, and humanities and social sciences environment around its regions [5
]. As a result, the complex CO2
emission situation leads to the great difficulty of CO2
mitigation. The difficulty of carbon emission reduction mainly comes from the spatial effects—such as spatial spillover and spatial heterogeneity—around provinces, caused by the negative externalities of CO2
], leading to the complexity of its driving mechanism. In order to clarify the CEI driving mechanism, traditional methods can provide only limited help, because they neglect the spatial interaction, which may lead to inaccurate estimate results. This kind of spatial interaction can be solved by spatial econometrics; that is, by introducing spatial weight variables to handle the spatial effects in regression.
Based on the above description of the background and necessity, this article conducts research on the temporal and spatial effects and influencing factors of CEI in 30 provinces in China, from 2005 to 2017. The global Moran index and the local Moran index of the panel data are measured, providing a detailed analysis of the spatial and temporal characteristics of 30 provinces. Besides this, a spatial panel econometric model including eight independent variables of GDP, URB, INS, ENS, ENI, TEL, OPL, and FDI, and one dependent variable of CEI is established, which supplements the vacancy of the research on the full-factor driving mechanism of carbon intensity. This model is superior in reflecting spatial effects, so it can provide more realistic estimation results. The rest of this article is organized as follows: Section 2
reviews the existing literature and previous research on CEI, and based on this, the main innovations and specific research contents of this research are further clarified. Section 3
describes the research methods and data sources, including the carbon intensity measurement, influential factors selection, spatial econometric analysis, and data sources and processing. Section 4
presents the results in the form of charts and graphs, and conducts a preliminary analysis and discussion based on the results. Section 5
shows the main conclusions and proposes some policy implications according to the results.
2. Literature Review
At present, there is a large amount papers on CEI regarding carbon reduction in different countries, which mainly focus on spatial and temporal characteristics, influencing factors, and prediction research, etc. Specifically, the research on the spatial and temporal characteristics of CEI mainly focuses on spatial difference [6
], the evolution of spatiotemporal patterns [8
], and spatiotemporal heterogeneity [10
], etc. The research on predictions regarding CEI focuses on the construction of predictive models [12
], the prediction of CEI reduction target achievements [14
], and scenario evolution analysis [17
]. Based on the researches above, more and more studies have focused on the factors affecting carbon intensity, and have committed to accurately identifying the driving mechanism of carbon intensity, improving emission reduction policies, and realizing low-carbon development.
The research on influencing factors has mostly focused on energy-related (ENR) and economy-related (ECO) factors, as economic growth, energy consumption and carbon emission are causal to each other, and there is a direct and inevitable connection [19
]. Fernandoa and Hor [20
] analyzed the impact of energy management on carbon emissions in Malaysian manufacturing based on PLS-SEM (Partial Least Squares-Structural Equation Modeling) software, and found that energy audits and energy efficiency are the key factors for the mitigation of carbon emissions. Vujović et al. [21
] used the neuro-fuzzy method to explore the relationship between energy, carbon intensity and economic growth. It was found that alternative energy has the greatest impact on carbon emission intensity. Some papers have also conducted research on the impact of urbanization (URB) on carbon emissions, as urbanization has a large demand for infrastructure construction, which has caused a lot of carbon emissions [22
]. Liu and Liu [5
] researched the spatial spillover effects of urbanization on carbon emissions in China by dividing China into Eastern, Middle and Western regions, in order to find the regional disparity. The results show that, as urbanization improves, its influence on local carbon emissions changes from positive to negative, and then the negative influence becomes weaker. With the development of economic globalization, factors related to international trade (INT), including import and export trade, foreign direct investment, etc., have become more and more important to the carbon emissions of countries and regions [23
]. Pan et al. [24
] studied the carbon intensity influencing factors of 34 OECD (Organization for Economic Co-operation and Development) countries based on genetic programming (GP) and symbolic regression methods (SRM), creatively using foreign direct investment as an influencing factor. In addition, technological innovation (TEI) is one of the main measures that can be used to reduce carbon emissions without reducing economic growth performance [25
]. Nguyen et al. [26
] explored the role of information and communication technologies and innovation in driving carbon emissions in 13 selected G-20 countries, based on quantile panel regression (QPR). Besides this, the impact of carbon emission trading (CET) [27
] and power (POW) factors [29
] on carbon intensity has been studied in recent years. The list of the research of related papers is shown in Table 1
. It can be seen that most studies only consider factors of one or two aspects, and few consider all of the aspects of factors as a system. This has led to relatively one-sided conclusions and limited reference for the overall planning and reduction of emissions. With respect to the research dimensions, most studies have only carried out research on the influential factors of CEI from a temporal dimension, and relatively few studies come from a comprehensively spatial-temporal dimension using space panel data.
The methods for the analysis of the driving mechanism of CEI in existing research are mainly based on the following three theories: decomposition analysis, machine learning, and spatial econometrics. Decomposition analysis is one of the classic theories that addresses influencing factors. It has been widely applied to the exploration of the factors that influence CO2
and other polluting gas emissions since the 1990s, and continues to be examined with respect to influential factors regarding CEI [35
]. It can be divided into the two main categories of index decomposition analysis (IDA) [23
] and structural decomposition analysis (SDA) [38
]. The advent of Machine Learning (ML) technology has greatly improved research on influencing factors, it can solve some of the problems which exist in traditional research, such as the selection of indicators and the limitation of large data processing. Therefore, it has been widely used in various research fields, including carbon intensity, in recent years [12
]. Although the above two mainstream methods have their own advantages in the research of carbon intensity factors, none of them can deal with complex spatial effects in theory, leading to a lack of research on the estimation of the influencing factors considering spatial effects.
Spatial econometric analysis, as a branch of econometrics, can handle spatial effects in regression models of panel data, such as spatial interaction and spatial autocorrelation. Since Anselin [42
] systematically structured the spatial econometric method, it has been widely used in economic policy-related research. With greater emphasis being placed on CO2
emissions-related research over the last few years, spatial econometric analysis has begun to be applied to the research on CEI. However, due to its relatively late appearance, few related studies are currently available. Zhang et al. [34
] studied the effect of urbanization on China’s CEI using the spatial Durbin mode, which is based on spatial econometric analysis, and comprehensively estimated the impact of population and land urbanization on carbon emissions, with consideration of the spatial effects. It has been proved that spatial econometric analysis can restore the actual spatial effects well [5
], but there are few studies on the application of this method to carbon intensity, and there is a lack of the comprehensive consideration of various factors on the driving mechanism of carbon intensity. This article aims to analyze the carbon intensity in China from a spatial-temporal dimension, and to explore the driving mechanism of carbon emission with the consideration of spatial effects. The complexity of the driving mechanism requires the consideration of various factors as comprehensively as possible, and the analysis of the temporal and spatial characteristics of carbon intensity can provide policy makers with a multi-angle reference. Spatial econometric analysis can provide a theoretical basis for spatial regression estimation, and has been applied in this article.
The main contributions and innovations of this article are as follows. (1) In order to supplement the vacancy of the research on the full-factor driving mechanism of carbon intensity, this paper selected the influencing factors of carbon intensity from five aspects: economy, energy, urbanization, international trade, and technology. (2) Spatial-temporal analysis was carried out based on the space penal data of China’s 30 provinces from 2005 to 2017; 2005 was selected as the starting point for the study period (the base year), as it is the year that China put forward carbon emission reduction commitments, and from then on China attached importance to emission reduction and strengthened emission reduction efforts. Although there have been some studies on China’s carbon intensity from spatial-temporal dimensions [5
], most of them focus on the analysis of three regions [5
], eight regions [33
] or others, and there is a lack of detailed analysis of the spatial and temporal characteristics of the 30 provinces. (3) Spatial econometric theory provides a theory for the spatial regression estimation of the influencing factors in this article, which can accurately describe the spatial interaction and spatial spillover effects that exist between the regions; this advantage is irreplaceable by other influencing factor research methods. Regression estimation which includes spatial variables can provide policymakers with more practical and effective emission reduction suggestions, and can provide an important reference for regional coordinated emission reduction. Besides this, the application of spatial econometrics to CEI influencing factors and the emission reduction strategies under regional coordination in China will provide a valuable reference to other countries facing similar problems.
In conclusion, in order to complete the research on the influential factors with respect to CEI in China from a provincial perspective, spatial econometrics is used as the theoretical support. First, we will perform global and local spatial autocorrelation tests on the CEI of 30 provinces in China (except Tibet, Hong Kong, Macao and Taiwan) between 2005 and 2017 in order to analyze the spatial effects. Then, a spatial Durbin panel model (SDPM) will be constructed, and the effect of the factors will be analyzed based on the estimation results. Finally, according to the analysis above, the provincial CEI driving mechanism will be discussed in depth, and realistic recommendations will be suggested for China’s regional cooperation emission reduction strategy in the context of regional coordinated development.
5. Conclusions and Policy Implications
Based on the theory of spatial econometrics, this paper has established panel data on the intensity of the carbon emissions and its influential factors of 30 provinces (municipalities and autonomous regions) in China between 2005 and 2017. The spatial effects, driving mechanisms, and regionally-coordinated emission reduction measures of the provincial carbon emission intensity were explored through the calculation of the Moran’s I and the construction of the SDPM model. The main conclusions are as follows: (a) China’s provincial carbon emission intensity has significant spatial autocorrelation characteristics, and it shows an increasing trend with time; (b) economic development has a negative correlation with carbon intensity, and the space spillover effect is significant, which is a great driving force for regional carbon intensity reduction; (c) energy intensity has a positive correlation with local carbon intensity, but the spatial spillover effect is negative. When reducing carbon intensity by adjusting energy intensity, the impact on surrounding areas should be fully considered. (d) The energy structure and urbanization have a relatively important effect on the intensity of carbon emissions, and have significant space spillover effects. Based on the analysis and discussion of the results, the following policy recommendations are proposed for regional collaborative carbon intensity emission reduction.
First, Low-CEI regions, such as Beijing, Tianjin and Zhejiang, can use their advantages with respect to economic development in order to drive the industrial transition of the surrounding regions and the other regions of the nation with high-CEI. They could fully mobilize their own technology and talent advantages to provide technical support for the development of clean energy and waste gas treatment in large resource-rich provinces, such as Xinjiang, Inner Mongolia, and Heilongjiang. Furthermore, with the state’s strategy of encouraging regional cooperation, regional resources can be shared, such as educational, cultural, tourism, and transportation resources.
Next, the CO2 emission quotas and emission reduction responsibilities allocated from a whole region would be more sensible for Low–High agglomeration regions, such as the Beijing–Tianjin–Hebei region. The responsibilities and quotas of the provinces should be distributed in the region as a whole, and should jointly shoulder the carbon intensity target assigned by the country. For regions that lie close to provinces with large resource reserves, their high CEI levels are often the result of the ease and convenience of procurement, and low transportation costs. These regions, such as Shaanxi and Hebei, bordering Shanxi; Qinghai and Gansu, bordering Xinjiang; and regions bordering Inner Mongolia, should focus on reducing their own demands for energy consumption by adjusting their industrial structures and transforming their underdeveloped production capacity. Simultaneously, other low-CEI regions that are not adjacent to high-carbon provinces with large resource reserves should assume more carbon emission reduction responsibilities as compensation, and should provide some support for the high emissions in high-carbon regions caused by their energy supply. In addition, high-CEI regions should work hard to advance the process of urbanization in a scientific and reasonable way, increase their talent introduction and research funding in order to promote the development of tertiary industry, and attract foreign direct investment, especially in energy production-related industries, which can bring advanced technologies, management models and cleaner production models from developed countries into the region.
Finally, for energy-dependent high-CEI regions—such as Shanxi, Shaanxi, Inner Mongolia, Ningxia, Xinjiang, Heilongjiang and Jilin—the energy intensity and coal consumption ratio are much higher, and the development of the whole nation depends on the resource endowment and energy industry in these regions. Based on these conditions, it is important to replace traditional fossil energy sources, such as coal, with clean energy sources, such as wind and nuclear energy. It is very important to develop solutions for the practical technical problems and policy issues that stand in the way of acceptance, such as the problem of grid connection and electricity pricing in wind power generation technology.