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Natural Hazards

, Volume 99, Issue 3, pp 1365–1380 | Cite as

Study of the impact of energy consumption structure on carbon emission intensity in China from the perspective of spatial effects

  • Hongwei XiaoEmail author
  • Zhongyu Ma
  • Peng Zhang
  • Ming Liu
Original Paper

Abstract

From now until 2030, China will be in a sprint to achieve reductions of 40–45% in carbon emission intensity by 2020 and 60–65% by 2030 compared to 2005; rigid requirements have thus been imposed for controlling carbon emission intensity. In this study, a spatial Durbin model that integrates a spatial lag model and a spatial error model is used to measure the degree of influence held by the energy consumption structure and other factors over carbon emission intensity and the spatial spillover effect. The results show that there is a spatial demonstration effect on the reduction in interregional carbon emission intensity in China. While the carbon emission intensity in the adjacent region decreases by 1%, the carbon emission intensity in this region will decrease by 0.05%, indicating that China’s regional low-carbon development model is also applicable to neighboring provinces and plays a large role in driving and demonstrating a low-carbon economy. Every additional 1% improvement toward optimizing the energy consumption structure enables the carbon emission intensity of the region to decrease by 0.21%; further, there is a positive spatial spillover effect driving carbon emission intensity decreases in neighboring areas of 0.25%. Industrial structure, energy intensity, energy price, and level of openness are the main factors influencing regional carbon emission intensity. According to the “14th Five-Year Plan,” there is an urgent need to optimize the energy consumption structure in the medium and long term and give full play to its ability to contribute to declines in carbon emission intensity.

Keywords

Energy consumption structure Spatial Durbin model Carbon emission intensity Demonstration-driving effect Spatial spillover 

1 Introduction

The Chinese government has placed great importance on addressing climate change issues and made major commitments to the international community. On November 26, 2009, at the World Climate Change Conference in Copenhagen, the Chinese government announced that its goal is to reduce carbon emission intensity by 40% to 45% by 2020 compared with 2005; on November 12, 2014, China announced in the Sino-US Joint Statement on Climate Change that carbon dioxide emissions would peak in approximately 2030 that they would strive to reach a peak earlier and that they would increase the proportion of nonfossil energy in primary energy consumption to approximately 20% by 2030; on June 30, 2015, China submitted a document on its national contribution to climate change to the UN Framework Convention on Climate Change Secretariat proposing that by 2030, China’s carbon emission intensity would fall by 60–65% compared with 2005; on November 30, 2015, President Xi Jinping reiterated China’s determination to make independent national contributions to carbon reduction in a speech at the Paris Climate Conference. From now until 2030, China is in a sprint to achieve a 40–45% reduction in carbon emission intensity by 2020, compared to 2005, and 60–65% by 2030; therefore, it is imposing rigid requirements to control carbon emission intensity.

The energy consumption structure is an important factor affecting carbon emissions. China has included structural indicators such as the nonfossil energy consumption proportion, natural gas consumption proportion, and coal consumption proportion in the energy development plan, so that the future impact of optimizing the energy consumption structure on the decline in carbon emission intensity will be more profound. A major issue is thus how to deeply understand the impact of energy consumption structure optimization on the decline in carbon emission intensity, accelerate that optimization under a green low-carbon development strategy, and optimize the energy consumption structure to make it the new focus of carbon emission intensity decline; these questions must be highly valued and urgently addressed in the “14th Five-Year Plan” and in the medium and long term.

How do the energy consumption structure and other factors affect the decline in carbon emission intensity and how much impact do they have? Many scholars have conducted extensive research focused on multiple or individual countries. Some research takes the perspective of sectors that affect carbon emission intensity, specifically the manufacturing, transportation, industrial, and residential sectors (Torvanger 1991; Greening et al. 1998, 1999, 2001; Greening 2004; Li 2013; Fan et al. 2013; Moutinho et al. 2014; Fan and Lei 2016). In quantitative research on the factors affecting carbon emission intensity, the research methods mainly include factor decomposition models and econometric models. Factor decomposition models tend to use the logarithmic mean decomposition method (LMDI) and the modified Laspeyres exponential decomposition method, and the research results show that energy intensity, the energy consumption structure, the carbon emission coefficient, industrial structure, and other factors have a great impact on carbon emission intensity (Torvanger 1991; Barnali Nag 2000; Nag and Parikh 2000; Ebohon and Ikeme 2006; Lu et al. 2007; Bhattacharyya and Matsumura 2010; Chen 2011; Hammond and Norman 2012; Alves and Moutinho 2013; Li and Ou 2013; Zhang and Da 2015; Liu et al. 2015; Chen and Lin 2015; Ang and Su 2016; Su and Ang 2016; Zhang et al. 2016; Jiang 2017; Sun et al. 2017; Tian et al. 2018). Econometric models primarily use time series data models, panel data models, and other models to study the impact of economic development, foreign direct investment, technological progress, population size, urbanization, and other factors on carbon emission intensity (Giblin and Mcnabola 2009; Sambodo and Oyama 2011; Fisher-Vanden et al. 2012; Fang et al. 2013; Wang and Ren 2013; Chik et al. 2013; Zhang et al. 2014; Ren et al. 2014; Wei and Zhou 2014; Ohlan 2015; Wang et al. 2015; Jovanović et al. 2015; Lin et al. 2015; Dong et al. 2016; Köne and Büke 2016; Lin and Nelson 2017; Li and Wang 2017; Jiao et al. 2018).

As China is seeing an increasing impact of its energy consumption structure on the decline in its carbon emission intensity, the literature including quantitative analysis has emerged in recent years. Most studies use factors such as urbanization, industrial structure, and technological progress, and methods such as factor decomposition models, influencing factors econometrics and regression models considering the national, provincial, and urban dimensions. The results from measuring the impact of the energy consumption structure on China’s carbon emission intensity show that it has a significant impact (Chen 2011; Liu 2013; Du 2013; Xu et al. 2013; Long et al. 2015; Cui et al. 2016; Wen et al. 2016; Yu et al. 2018).

Most studies reviewed in the scientific literature suggest that the optimization of the energy consumption structure has a significant positive correlation with the decline in carbon emission intensity, but they do not take into account the spatial interactions between the energy consumption structure and carbon emission intensity for China’s regions. Given the spatial correlation between regions and the impact of the energy consumption structure on carbon emissions intensity, spatial effects are not considered, and it brings a certain degree of bias to the empirical results. This paper used a spatial econometric model to control the factors that affect the carbon emission intensity of per capita GDP, the proportion of secondary and tertiary industry, the level of urban development (urbanization), energy intensity, energy prices, level of openness, and foreign direct investment. First, using quantitative methods, we measured and then decomposed the impact of the energy consumption structure and other factors on the decline in carbon emission intensity. We then explored the mechanism behind the spatial effects of the energy consumption structure on carbon emission intensity under the new era. The goal of this paper was to determine how to fully benefit from the contribution of energy consumption structure optimization to the decline in carbon emission intensity, and the findings have important significance for guiding policy.

2 Model building

Carbon emission intensity is affected by various factors, such as economic development, energy consumption structure, industrial structure, urban and rural structure, technological progress, the price of energy, and the level of openness. Referring to the existing body of research, this paper uses energy consumption based on carbon emission intensity as the dependent variable. Nine additional factors, including structure, per capita GDP, proportion of secondary industry, proportion of tertiary industry, urbanization level, energy intensity, energy price, level of openness, and foreign direct investment, are used as independent variables to construct the following formula for determining the energy consumption structure. The model for the impact of carbon emission intensity is as follows:
$$ \begin{aligned} & {\text{CI}}_{it} = \alpha_{i} + \beta_{1} {\text{ES}}_{it} + \beta_{2} {\text{PGDP}}_{it} + \beta_{3} {\text{IS}}2_{it} + \beta_{4} {\text{IS}}3_{it} + \beta_{5} {\text{UR}}_{it} \\ & \quad \quad \quad + \,\beta_{6} {\text{EI}}_{it} + \beta_{7} {\text{EP}}_{it} + \beta_{8} {\text{OP}}_{it} + \beta_{9} {\text{FDI}}_{it} + \varepsilon_{it} \\ \end{aligned} $$
(1)

The variables in the formula are defined below:

CI—carbon emission intensity, expressed in terms of CO2 emissions per unit of GDP, in units of 10,000 tons carbon/100 million yuan. CI is primarily used to measure the relationship between a country’s economy and its carbon emissions. If a country’s economic growth is accompanied by a decline in its carbon emission intensity, this indicates that the country exhibits a low-carbon development model. To ensure that the data between different provinces are comparable, the GDP data from the previous years were uniformly adjusted to the 2005 price using the GDP index.

ES—energy consumption structure, expressed as the proportion of coal in the total consumption of coal, oil, and natural gas in the subregion, stated as a percentage.

PGDP—GDP per capita, measured by economic growth per capita GDP in the subregion, in units of yuan per person. Using per capita GDP can effectively avoid the large difference between the total GDP and the population in different regions. To ensure that the data between different provinces are comparable, the per capita GDP index was used to adjust the variable to 2005 prices.

IS2—proportion of secondary industry, as expressed by its percentage of the composition of regional GDP.

IS3—proportion of tertiary industry, as expressed by its percentage of the composition of regional GDP.

UR—level of urban development, as expressed by the urbanization rate of the resident population, measured as a percentage. At present, the urbanization rate is the primary indicator for measuring and evaluating the level of urbanization in a region. The urbanization rate of the resident population well reflects the increase in the number and scale of towns and cities. This measure of expansion reflects the process and extent of growing populations over a certain period of time.

EI—energy intensity, as expressed in terms of energy consumption per unit of GDP, in units of 10,000 tons of standard coal/100 million yuan. Energy intensity is one of the most commonly used indicators for comparing the comprehensive efficiency of energy utilization in different countries and regions. It should reflect the technical level to a certain extent: when improvements are made to the level of energy-saving and emission reduction technology, energy intensity tends to decline. To ensure that the data between different provinces were comparable, as with the carbon emission intensity variable, the GDP data from previous years were uniformly adjusted to 2005 prices.

EP—energy price, expressed by the ex-factory price index of industrial products in various regions (previous year = 100). The ex-factory price index reflects the trends and variations in the total ex-factory price of all industrial products over a certain period of time. To some extent, the relative number can reflect the energy price level. To ensure that the data between different provinces are comparable, the ex-factory price index was used to adjust the variable to 2005 prices.

OP—level of openness, as the total import and export volume of each region as a percentage of GDP. The ratio of total imports and exports to GDP is a measure of the degree of openness that effectively avoids large differences in value between different regions due to the differences in import and export scale and the scale of GDP.

FDI—foreign direct investment, as expressed by the proportion of investment from foreign-invested enterprises registered at the end of each year to GDP and stated as a percentage. The proportion of foreign-invested enterprises’ total investment in GDP is an important indicator measuring the level of foreign investment utilization. It effectively avoids the large numerical differences caused by differences in the investment scale and the GDP scale of foreign-invested enterprises in different regions, and simultaneously, maintains the difference between years. The data are comparable, and the total investment of foreign-invested enterprises is converted into RMB using the average exchange rate for that year.

The sample studied in this paper is from 30 provinces, municipalities, and autonomous regions in China from 2005 to 2016 (due to the lack of Tibetan energy statistics, Tibet is not included); the data include GDP, GDP per capita, industrial structure, urbanization rate, industrial product ex-factory price index, the total export value, and the total investment of foreign-invested enterprises, which were all derived from the China Statistical Yearbook 2017. The energy consumption data come from the China Energy Statistical Yearbook 2017. The carbon emissions data were derived from China’s emissions accounts and datasets. In the model shown by Eq. (1), ES, IS2, IS3, UR, FDI, and OP are all percentages, and we use the variables directly; CI, PGDP, EI, EP are level values, and we use their logarithmic values.

3 Empirical analysis

3.1 Space effect test

Before constructing a spatial econometric model of the impact of the energy consumption structure on carbon emission intensity, a series of spatial Lagrangian tests were used to verify the rationality of introducing spatial effects into the common panel data model. This paper uses the spatial lag-dependent Lagrangian test LM-LAG proposed by Anselin (1987, 2001, 2007), the spatial error-dependent Lagrangian test LM-ERR and Elhorst’s (2005, 2016) and Piras et al. (2010). Lagrangian test R-LM-LAG for spatial lag robustness, and the Lagrangian test for spatial error robustness R-LM-ERR. The significance of the daily test statistic determines whether to choose the spatial econometric model; if the results of LM-LAG, LM-ERR, R-LM-LAG, R-LM-ERR are not significant, it is not appropriate to choose a spatial econometric model; if they are significant, then a spatial econometric model should be used to capture the spatial correlation of the impact of energy consumption structure on carbon emission intensity. The time fixed effect was combined with the significance test likelihood ratio (LR) to further investigate whether there were spatial and temporal fixed effects. The results for the 2005–2016 panel data from the model estimation of the energy consumption structure did not take spatial factors into account. At a 5% significance level, the LM-LAG and LM-ERR statistics of the mixed OLS model, the space fixed effect model, and the time fixed effect model were significant, and the R-LM-ERR statistics of the time fixed effect model were significant. At the 10% significance level, the R-LM-LAG statistics for the time fixed effect model and the space–time fixed effect model were significant, so it is reasonable to introduce the spatial effect when studying the impact of the energy consumption structure on carbon emission intensity. The space–time fixed effect combined with the significance test LR showed that the energy consumption structure had a fixed effect on carbon emission intensity, which further demonstrated that spatial econometric models can better capture the impact of energy consumption structure on the decline in carbon emission intensity (Table 1).
Table 1

Panel data model estimation results of the impact of energy consumption structure on carbon emission intensity for 2005–2016, without considering spatial factors

Variable

Mixed OLS

Space fixed effect

Time fixed effect

Space–time fixed effect

Constant

− 0.2564 (− 0.5778)

ES

0.0094*** (13.2729)

0.0103*** (9.9562)

0.0095*** (13.5562)

0.0101*** (10.0180)

PGDP

0.0946** (2.3220)

− 0.1176* (− 1.8822)

0.1607*** (2.9088)

− 0.1607* (− 1.6823)

IS2

− 0.0176*** (− 6.1241)

0.0115*** (2.8711)

− 0.0180*** (− 6.3218)

0.0107*** (2.7182)

IS3

− 0.0205*** (− 6.2944)

0.0079* (1.6963)

− 0.0191*** (− 5.6450)

0.0061 (1.3230)

UR

0.0048** (2.3165)

0.0029 (0.9455)

0.0031 (1.3636)

0.0011 (0.3736)

EI

0.9288*** (37.0103)

0.6824*** (11.9210)

0.9229*** (36.1932)

0.7212*** (12.8643)

EP

0.1744**(2.4001)

0.1196* (1.8048)

0.2250** (2.5085)

0.1358 (1.5571)

OP

0.0003 (0.7553)

0.0006 (1.5853)

− 0.0000 (− 0.0756)

0.0011** (2.5062)

FDI

0.0006*** (3.1732)

0.0001 (1.2361)

0.0007*** (3.2545)

0.0001 (1.2488)

\( \sigma^{2} \)

0.0215

0.0047

0.0208

0.0042

\( R^{2} \)

0.9305

0.8798

0.9256

0.5718

Adjusted \( R^{2} \)

0.9287

0.8771

0.9239

0.5621

Durbin–Watson

2.4139

1.6619

2.5110

1.8539

Likelihood value

185.2270

459.7136

190.7811

477.3570

LM spatial lag

6.9797 (0.008)

5.0951 (0.024)

11.0037 (0.001)

1.2260 (0.268)

Robust LM spatial lag

2.2628 (0.133)

0.1718 (0.679)

2.8541 (0.091)

3.0583 (0.080)

LM spatial error

6.6003 (0.010)

5.6911 (0.017)

12.5379 (0.000)

0.0662 (0.797)

Robust LM spatial error

1.8834 (0.170)

0.7678 (0.381)

4.3883 (0.036)

1.8986 (0.168)

LR test joint Significance

Fixed effects

Statistics

Degrees of freedom

Probability

Spatial fixed effects

573.1519

30

0.0000

Time-period fixed effects

35.2869

12

0.0004

*, **, and *** are significant at the 10%, 5%, and 1% levels, respectively, and the t value or z value is in the brackets below the model estimation parameters. The space Lagrangian test statistic below the brackets is the P value

3.2 Empirical test

To comprehensively and objectively reflect the spatial effect of the energy consumption structure on carbon emission intensity, first, a spatial Durbin model is established combining spatial lag and spatial error. This is followed by a Wald test based on spatial lag dependence, Wald-LAG; a spatial error-dependent Wald test, Wald-ERR; a likelihood ratio test of spatial lag dependence, LR-LAG; a likelihood ratio of spatial error dependence, LR-ERR. To determine whether the SDM can be reduced to a spatial lag or spatial error model, the rule is that if Wald-LAG and LR-LAG are significant, while Wald-ERR and LR-ERR are not significant, then the SDM can be reduced to a spatial lag model; if Wald-ERR and LR-ERR are significant, while Wald-LAG and LR-LAG are not significant, it can be simplified to a spatial error model; and if Wald-ERR, LR-ERR, Wald-LAG, and LR-LAG are all significant, a SDM combining spatial lag and spatial error should be selected. The SDM for the impact of energy consumption structure and other factors on carbon intensity is:
$$ \begin{aligned} & {\text{CI}}_{it} = \delta \sum\limits_{j = 1}^{N} {w_{ij} } {\text{CI}}_{it} + \alpha + \beta_{1} {\text{ES}}_{it} + \beta_{2} {\text{PGDP}}_{it} + \beta_{3} {\text{IS}}2_{it} + \beta_{4} {\text{IS}}3_{it} \\ & \quad \quad \quad + \,\beta_{5} {\text{UR}}_{it} + \beta_{6} {\text{EI}}_{it} + \beta_{7} {\text{EP}}_{it} + \beta_{8} {\text{OP}}_{it} + \beta_{9} {\text{FDI}}_{it} + \theta_{1} \sum\limits_{j = 1}^{N} {w_{ij} } {\text{ES}}_{ijt} \\ & \quad \quad \quad + \,\theta_{2} \sum\limits_{j = 1}^{N} {w_{ij} } {\text{PGDP}}_{ijt} + \theta_{3} \sum\limits_{j = 1}^{N} {w_{ij} } {\text{IS}}2_{ijt} + \theta_{4} \sum\limits_{j = 1}^{N} {w_{ij} } {\text{IS}}3_{ijt} + \theta_{5} \sum\limits_{j = 1}^{N} {w_{ij} } {\text{UR}}_{ijt} \\ & \quad \quad \quad + \,\theta_{6} \sum\limits_{j = 1}^{N} {w_{ij} } {\text{EI}}_{ijt} + \theta_{7} \sum\limits_{j = 1}^{N} {w_{ij} } {\text{EP}}_{ijt} + \theta_{8} \sum\limits_{j = 1}^{N} {w_{ij} } {\text{OP}}_{ijt} + \theta_{9} \sum\limits_{j = 1}^{N} {w_{ij} } {\text{FDI}}_{ijt} + \mu_{i} + \lambda_{t} + \varepsilon_{it} \\ \end{aligned} $$
(2)
\( {\text{CI}}_{it} \) is the carbon emission intensity of region i in year t; \( {\text{ES}}_{it} \), \( {\text{PGDP}}_{it} \), \( {\text{IS}}2_{it} \), \( {\text{IS}}3_{it} \), \( {\text{UR}}_{it} \), \( {\text{EI}}_{it} \), \( {\text{EP}}_{it} \), \( {\text{OP}}_{it} \), and \( {\text{FDI}}_{it} \) are the indicator values of factors related to carbon emission intensity, respectively, energy consumption structure, per capita GDP, proportion of secondary industry, proportion of tertiary industry, level of urbanization, energy intensity, energy price, level of openness, and foreign direct investment of region i in year t.

\( \alpha \) is constant; \( \theta \) and \( \beta \) are similar fixed \( 9 \times 1 \)-dimensional model parameter estimation vectors; \( \mu_{i} \) and \( \lambda_{t} \) are space and time fixed effects, respectively; \( w_{ij} \) are the space weights of the \( (i,j) \) elements in matrix W.

The construction of the spatial weight matrix W directly affects the interpretability of the parameter estimation results of the spatial econometric model. At present, there are many methods for constructing a spatial weight matrix. For example, a geospatial weight matrix is mainly constructed according to spatial distance, and an economic space weight matrix is mainly calculated based on the interregional empirical flow matrix (trade volume, capital flow, etc.), traffic convenience, and the similarity of levels of economic development. To more accurately capture the spatial correlation of the impact of the interregional energy consumption structure on carbon emission intensity, this paper uses a spatial weight matrix that makes use of both geographic information and economic information. The geospatial weight matrix is constructed using the inverse ratio method of large circle distance:
$$ W_{ij}^{\text{GS}} = \left\{ {\begin{array}{*{20}c} {\frac{1}{{\left\{ {R \times \text{arc} \,cos[\sin x_{i} \sin x_{j} + \cos x_{i} \cos x_{j} \cos (y_{i} - y_{j} )]} \right\}^{\alpha } }}} & {i \ne j} \\ 0 & {i = j} \\ \end{array} } \right. $$
(3)
where \( R \times {\text{arc}}\,\cos [\sin x_{i} \sin x_{j} + \cos x_{i} \cos x_{j} \cos (y_{i} - y_{j} )] \) is the large circle distance of spatial positions \( A_{i} (x_{i} ,y_{i} ) \) and \( A_{j} (x_{j} ,y_{j} ) \); R is the radius of the earth; \( x_{i} ,y_{i} \) are the geological coordinates of the provincial capitals; \( \alpha \) is a suitable parameter, usually taking a value of 1 or 2 (to avoid negative numbers, the value used in this paper is 2); and the corresponding space weight is the square of the reciprocal distance.
The economic spatial weight matrix uses the similarity of economic development levels across spatial geographic units to reflect adjacent spatial, nongeographical factors.
$$ E_{ij} = \left\{ {\begin{array}{*{20}c} {\frac{1}{{\left| {\bar{G}_{i} - \bar{G}_{j} } \right|^{\alpha } + m}}} & {i \ne j} \\ 0 & {i = j} \\ \end{array} } \right.,\quad \bar{G}_{i} = \frac{1}{12}\sum\limits_{t = 2005}^{2016} {G_{it} } $$
(4)

Among them, \( G_{it} \) is the per capita GDP and represents the economic development level of region i in year t (calculated as the constant price in 2005); \( \alpha \) is the adjustment parameter for the economic weight. (As before, this variable usually takes a value of 1 or 2; this paper assumes a value of 2.) When the GDP per capita is the same for two different regions in the same time period, the denominator is 0. When any two different regions have the same GDP per capita in the same time period, then m = 1; when any two different regions have a different GDP per capita in the same time period, then m = 0.

A spatial weight matrix containing both geographic information and economic information is \( W^{*} = W^{\text{GS}} \times E \), which is the point multiplication of the geographic and economic spatial weight matrix.

From the results of the SDM estimation for the impact of the energy consumption structure on carbon emission intensity in 2005–2016, it can be seen that the Wald-LAG, LR-LAG, Wald-ERR, and LR-ERR (Lee and Yu 2010) statistics in the space–time fixed effect model and time–space fixed effect model after bias correction were significant at a level below 5%, and the Wald-LAG and Wald-ERR statistics in the spatial random and time fixed effect model were also significant, which indicates that the SDM cannot be reduced to a spatial lag or a spatial error model. Therefore, the SDM combining spatial lag and spatial error will better characterize the spatial impact of the energy consumption structure on carbon emission intensity. The phi parameter test results constructed by Baltagi (2013) show that the phi parameter value is significant at the 1% level, indicating that the fixed effect and random effect estimation result will be significantly different; the panel data space Hausman test results show that the spatial random and time fixed effect model was rejected, which further confirms that the space–time fixed effect model is more reasonable than the spatial random and time fixed effect model. The SDM estimates show that the goodness fit of the space–time fixed effect model and space–time fixed effect model after bias correction is better than that of the spatial random and time fixed effect models; further, the coefficients of energy consumption structure, per capita GDP, secondary industry proportion, tertiary industry proportion, urbanization level, energy intensity, energy price, openness, foreign direct investment, and other factors affecting carbon emission intensity are in line with expectations. Therefore, it is reasonable to choose a space–time fixed effect model to measure the impact of the energy consumption structure on the carbon emission intensity.

In Table 2 space–time fixed effect model (deviation correction) column results show that there is a spatial effect between the energy consumption structure and carbon emission intensity. The W*CI coefficient is negative and significant at the 10% significance level, indicating that there is a spatial demonstration effect on the reduction of interregional carbon emission intensity. If carbon emission intensity in the adjacent region decreases by 1%, carbon emission intensity in the focal region will decrease by 0.1%. This result indicates that China’s regional low-carbon development model has played a large role in driving and demonstrating reduction in the surrounding provinces, leading regions toward green and low-carbon development. The impact of the energy consumption structure on carbon emission intensity shows that every 1 percentage point decrease in coal consumption will drive carbon emission intensity down by 0.008%, and the spatial spillover effect on adjacent areas is significantly positive, driving a decline in the carbon emission intensity in adjacent areas of 0.007%. This indicates that given a background of optimizing and upgrading the energy structure, all provinces in China have adhered to a path of green low-carbon development and have vigorously implemented dual controls over total energy consumption and consumption intensity, strictly controlling coal consumption, increasing the proportion of natural gas and nonfossil energy consumption, and achieving a win–win situation in terms of economic development, energy conservation, and emission reduction. This strategy has effectively promoted the decline in local carbon emission intensity and brought positive spatial spillover effects to support the reduction of carbon emission intensity in surrounding areas. The demonstration of energy consumption structure optimization has indirectly promoted the decline in carbon emission intensity in surrounding areas.
Table 2

Estimated results of the spatial Durban panel data model for the impact of energy consumption structure on carbon emission intensity for 2005–2016

Variable

Space–time fixed effect

Space–time fixed effect (deviation correction)

Spatial random, time fixed effect

W*CI

0.0530 (0.8918)

0.0969* (1.6452)

0.0200 (0.3351)

ES

0.0078*** (7.3188)

0.0078*** (6.8316)

0.0069*** (6.9102)

PGDP

− 0.3085*** (− 2.7754)

− 0.3043*** (− 2.5806)

− 0.1319 (− 1.2833)

IS2

0.0142*** (3.2902)

0.0142*** (3.0985)

0.0064* (1.7087)

IS3

0.0075 (1.6279)

0.0075 (1.5386)

0.0021 (0.5075)

UR

0.0054 (1.3471)

0.0055 (1.2948)

0.0045 (1.2637)

EI

0.8516*** (12.8895)

0.8538*** (12.1825)

0.8652*** (14.8436)

EP

0.0199(0.2144)

0.0165 (0.1679)

0.0705 (0.7635)

OP

0.0007 (1.4491)

0.0007 (1.2727)

0.0003 (0.5546)

FDI

0.0001 (1.2800)

0.0001 (1.1478)

0.0001 (0.8003)

W*ES

0.0074*** (3.2390)

0.0070*** (2.8990)

0.0046** (2.3230)

W*PGDP

− 0.4209** (− 2.3238)

− 0.4150** (− 2.1598)

− 0.0021 (− 0.0139)

W*IS2

0.0022 (0.3353)

0.0017 (0.2442)

− 0.0033 (− 0.5549)

W*IS3

0.0020 (0.2552)

0.0017 (0.2068)

0.0008 (0.1088)

W*UR

− 0.0117** (− 1.9637)

− 0.0117* (− 1.8518)

− 0.0073 (− 1.3831)

W*EI

− 0.1406 (− 1.1524)

− 0.1728 (− 1.3460)

− 0.0809 (− 0.8052)

W*EP

0.2830(1.6105)

0.2795(1.4989)

0.2663 (1.6170)

W*OP

0.0020*** (2.9420)

0.0020*** (2.7353)

0.0018*** (2.7213)

W*FDI

0.0005** (2.4636)

0.0005** (2.2915)

0.0004** (2.0962)

π

0.1154*** (5.5076)

\( \sigma^{2} \)

0.0037

0.0041

0.0042

\( R^{2} \)

0.9879

0.9879

0.9848

Squared correlation coefficient

0.6195

0.6191

0.8928

Likelihood value

499.1390

499.1390

411.3910

Wald test spatial lag

44.5163 (0.000)

39.1293 (0.000)

29.1416 (0.001)

LR test spatial lag

42.0697 (0.000)

42.0697 (0.000)

NA

Wald test spatial error

46.2138 (0.000)

41.1916 (0.000)

30.0973 (0.000)

LR test spatial error

43.4659 (0.000)

43.4659 (0.000)

NA

Hausman test

Statistics

Degrees of freedom

Probability

40.2480

19

0.0030

*, **, and *** are significant at the 10%, 5%, and 1% levels, respectively, and the t value or z value is in the brackets below the model estimation parameters. The space Lagrangian test statistic below the brackets is the P value

Investigating other factors that influence the intensity of carbon emissions shows that per capita GDP, proportion of secondary industry, and energy intensity are the main factors affecting regional carbon emission intensity. Every 1% increase in per capita GDP reduces the carbon emission intensity by 0.30%, and the spatial spillover effect on adjacent areas is negative, driving a decrease of 0.42%; these results indicate that economic development is conducive to the decline in carbon emission intensity and can achieve both energy-saving and emission reduction in local and surrounding areas in conjunction with economic growth. For every 1 percentage point decrease in the proportion of secondary industry, the carbon emission intensity will decrease by 0.01%; further, the spatial spillover effect on adjacent areas is positive, driving a 0.02% decrease in the carbon emission intensity in adjacent areas and indicating that the industrial structure is following a gradual path toward optimization. With the adjustment, the proportion of heavy industry has gradually declined, and the decline in the proportion of secondary industry has brought positive effects through the decline in carbon emission intensity in the local and surrounding areas. A 1% reduction in energy intensity will drive a 0.85% reduction in carbon emission intensity; this impact is much higher than that from other factors, indicating that using technological advancement to improve energy efficiency and reduce energy intensity is the most effective way to reduce carbon emission intensity. However, the decline in energy intensity has a significantly negative effect on the spatial spillover effect on adjacent areas, meaning that it is not conducive to the reduction of carbon emission intensity in adjacent areas. This result indicates that there is a competitive relationship between energy conservation and emissions reduction in different regions and that the willingness to promote the application of energy conservation and emission reduction technologies is low.

The direct impacts of tertiary industry proportion, urbanization development level, energy prices, level of openness, foreign direct investment, and other factors on carbon emission intensity are not significant, but their indications in terms of orientation, propensity, and forward-looking issues must be given high priority. The spatial spillover effects of the urbanization development level, level of openness, foreign direct investment, and other factors on carbon emission intensity are significant and deserve focus. The tertiary industry proportion, energy prices, level of openness, and foreign direct investment will increase carbon emission intensity, and the spatial spillover effect on adjacent areas is positive, which is not conducive to the decline in carbon emission intensity in adjacent areas. These results show that China is still in the initial stage of developing a service economy. To promote the rapid development of the service industry, China has continued the extensive mode of original industrial development, but in the development process, the application of environmental regulations and environmental technologies to the service industry has been neglected. The energy price system still lacks a mechanism, and the degree of greening while opening up to the outside world and attracting foreign direct investment is low. For every 1% increase in the level of urbanization, the carbon emission intensity will increase by 0.008%, but the space spillover effect on adjacent areas is negative, thus helping the carbon intensity of adjacent areas to decline.

3.3 Impact effect decomposition

The measurement methods proposed by Lessage and Pace (2009) to measure the impacts of energy consumption structure, per capita GDP, secondary industry share, tertiary industry share, urbanization development level, energy intensity, energy price, openness, foreign direct investment, and other factors on carbon emission intensity were further used to decompose them into direct and indirect effects. Tables 3 and 4 contain the influence effect decomposition results for the SDM model and the deviation-corrected SDM model, respectively, and it can be seen that the results of the two methods are more similar. In the SDM model, the direct effects of energy consumption structure, per capita GDP, secondary industry share, and energy intensity on the decline in carbon emission intensity are significantly positive, indicating that China’s energy consumption structure adjustment, increase in economic development, optimization and upgrading of industrial structure, energy conservation, and emission reduction technology advancement have played an important role in reducing the intensity of carbon emissions in the past 10 years. The direct effect of the increasing proportion of tertiary industry, the rapid progress of urbanization, rising energy prices, steadily advancing opening up, and rising foreign direct investment on the increase in carbon emission intensity is positive, although not significant; this implies a policy focus on the future decline of carbon emission intensity. Considering the indirect effects shown in the SDM model, the impact of energy consumption structure, per capita GDP, urbanization development level, openness, and foreign direct investment on the decline in carbon emission intensity is significant, indicating that there is a strong spatial spillover effect on the impact of energy consumption structure and other factors on the decline in carbon emission intensity.
Table 3

Effect of energy consumption structure and other factors on carbon emission intensity under the spatial Durbin panel data model

Variable

Direct effect

Indirect effect

Total effect

ES

0.0080*** (7.0944)

0.0082*** (3.0769)

0.0162*** (5.4046)

PGDP

− 0.3235** (− 2.7404)

− 0.4721** (− 2.2708)

− 0.7956*** (− 3.5389)

IS2

0.0143*** (3.1887)

0.0028 (0.3776)

0.0171* (1.8561)

IS3

0.0077 (1.5955)

0.0022 (0.2400)

0.0099 (0.8939)

UR

0.0053 (1.2608)

− 0.0122* (− 1.8416)

− 0.0070 (− 1.2683)

EI

0.8497*** (12.2604)

− 0.0945 (− 0.7779)

0.7551*** (5.8852)

EP

0.0244 (0.2470)

0.3079 (1.5195)

0.3324 (1.3546)

OP

0.0008 (1.4697)

0.0022*** (2.7453)

0.0030*** (2.9887)

FDI

0.0002 (1.3140)

0.0005** (2.3683)

0.0007** (2.5075)

Table 4

Effect of energy consumption structure and other factors on carbon emission intensity under the spatial model of the Durbin panel after bias correction

Variable

Direct effect

Indirect effect

Total effect

ES

0.0080*** (6.8590)

0.0083*** (3.2217)

0.0163*** (5.5959)

PGDP

− 0.3200** (− 2.7184)

− 0.4846** (− 2.2322)

− 0.8045*** (− 3.3963)

IS2

0.0143*** (3.0175)

0.0032 (0.4308)

0.0175* (1.8589)

IS3

0.0075 (1.4986)

0.0023 (0.2554)

0.0099 (0.8851)

UR

0.0050 (1.2024)

− 0.0120* (− 1.7880)

− 0.0070 (− 1.2325)

EI

0.8505*** (12.1395)

− 0.0942 (− 0.7477)

0.7563*** (5.5593)

EP

0.0256 (0.2566)

0.3018 (1.4879)

0.3274 (1.3605)

OP

0.0007 (1.4212)

0.0022*** (2.7931)

0.0030*** (3.0978)

FDI

0.0002 (1.3149)

0.0005** (2.3577)

0.0007** (2.4792)

Regardless of the spatial effects, the empirical analysis of the impact of energy consumption structure on carbon emission intensity may bring with it some bias. The indirect effect of energy consumption structure optimization, an increase in economic development, and rapid progress in urbanization on the decline in carbon emission intensity is significantly positive, which indicates a demonstrable optimization of the energy consumption structure, economic growth, and urbanization development in China that has been conducive to the decline in carbon emission intensity. The indirect effect of the steadily advancing opening up and rising foreign direct investment on the decline in carbon emission intensity is significantly negative, further reflecting that the concept of green low-carbon development has not been sufficiently embedded in the process of opening up and attracting foreign direct investment. Industrial structure adjustment, energy intensity decline, and energy price increase are negative for the indirect effect of carbon emission intensity decline. Although it is not significant, this implies a policy focus toward indirectly affecting a decline in carbon emission intensity.

From a decomposition perspective, China has adopted high efficiency, low pollution by accelerating power supply and power grid construction, the cogeneration of heat and power, increased nonfossil energy input, and an expanded natural gas supply. As the consumption structure has been significantly optimized, the proportion of energy consumption has further increased, and the consumption of coal has decreased significantly. The energy consumption structure has a significant direct effect on carbon emission intensity. The energy consumption structure was optimized by 1 percentage point, directly driving the carbon emission intensity down by 0.008 percentage points. The indirect effect of energy consumption structure optimization on the decline in carbon emission intensity is significantly positive, indicating that there are demonstration bands between different regions, and the indirect effect is greater than the direct effect. In the “14th Five-Year Plan” and in the medium and long term, it is critical to continue to optimize the energy consumption structure and allow it to fully contribute to the decline in carbon intensity.

4 Conclusions and policy implications

Between today and 2030, China aims to reduce carbon emission intensity by 60–65% compared to 2005, and rigid requirements have been imposed to control carbon emission intensity. China’s economy has entered a high-quality development stage, and thus, both the government and academia are focusing on how to achieve the goal of carbon emission intensity control through energy consumption structure optimization. To develop targeted policy recommendations based on empirical evidence, research should be conducted that examines the spatial effects and impact of energy consumption structure on carbon emission intensity while controlling various influencing factors, as described in this paper. The basic conclusions and policy implications of this paper are as follows:
  1. 1.

    Through an empirical analysis of the SDM model, a spatial effect on the reduction of regional carbon emission intensity in China was demonstrated. If carbon emission intensity in the adjacent region decreases by 1%, carbon emission intensity in the focal region will decrease by 0.1%. China’s regional low-carbon development model has played a significant role in driving and demonstrating low-carbon strategies to the surrounding provinces. Therefore, the “14th Five-Year Plan” and medium and long-term policies should attach great importance to regional low-carbon development. While promoting the rapid development of the service industry in an orderly manner, China should focus on building low-carbon industrial and transportation systems and low-carbon buildings.

     
In the field of industrial production, a number of viable steps can be undertaken to accelerate the cultivation of solutions that have been shown to reduce carbon emission intensity. These include accelerating the cultivation of green and low-carbon industries; vigorously developing energy-saving and environmentally friendly manufacturing and service industries; promoting technological progress and the low-carbon transformation of key industries such as petrochemical, steel, building materials, equipment and nonferrous metals; and improving backward production capacity. The elimination of equipment can strengthen corporate energy management. In the field of transportation, China should optimize the transportation network layout plan, use ICT (information communications technology) to build a smart traffic management system, promote the use of new energy-saving or high-efficiency vehicles, and comprehensively improve the efficiency and management level of transportation services. In the field of construction, it is necessary to strengthen supervision over buildings’ complete life cycle, including building quality, energy conservation, design, construction, use, maintenance, and demolition. At the same time, there is a need to increase renovations to improve energy-saving and heat insulation of existing buildings.
  1. 2.

    After fully considering the spatial effect, the energy consumption structure affects the decline in carbon emission intensity through direct and indirect pathways. Directly, for every 1% additional optimization of the energy consumption structure, the carbon emission intensity decreases by 0.008%. Indirectly, the spatial spillover effect in neighboring areas is positive, so that the carbon emission intensity in these adjacent areas is also reduced by 0.008%, indicating the presence of demonstration bands between regions. Therefore, in the “14th Five-Year Plan” and for medium and long-term strategy, it is critical to optimize the energy consumption structure and give full play to its ability to contribute to declines in carbon emission intensity. As a first step, we propose a reduction in the total coal consumption in the Beijing–Tianjin–Hebei, Yangtze River Delta, and Pearl River Delta regions. By 2020, the three provinces of Beijing, Tianjin, and Hebei should strive to reduce net coal consumption by more than 100 million tons compared to 2012; the Pearl River Delta region already aims to achieve the goal of negative growth ahead of schedule. Second, China should vigorously strengthen the development and promotion of nonfossil energy and actively engage in the centralized and distributed development of hydro, nuclear, wind, and solar power in accordance with the principle of combining centralized output with local consumption and utilization and thereby achieve the goal of 20% nonfossil energy in primary energy consumption by 2030.

     
  2. 3.

    From the perspective of other influencing factors, industrial structure and energy intensity are the main factors affecting regional carbon emission intensity. Therefore, in the “14th Five-Year Plan” and medium and long term, China should adhere to the two-wheel drive of technological innovation and institutional innovation. Only by making major breakthroughs in the innovation and application of low-carbon technologies can China achieve its 2030 China carbon emission intensity control strategic objective. On the one hand, it is necessary to study and develop a low-carbon technology promotion catalog, vigorously promote economically applicable low-carbon technologies, and focus on promoting clean coal power generation, waste heat and residual pressure utilization, the cogeneration of heat and power, high-efficiency motors, green lighting, green buildings, energy-saving and new energy vehicles. For example, low-carbon technology demonstrates and promotes key low-carbon technology equipment and increases the research and development and reserves of forward-looking key low-carbon technologies, especially coal gasification cycle combined power generation, carbon capture and storage; in particular, storage can be combined with technologies such as fuel cell vehicles, advanced nuclear power, and distributed smart grids. Relying on scientific research institutes and enterprises to establish low-carbon technology incubators improves the mechanism for the transformation of low-carbon technology achievements.

     

Notes

Acknowledgements

This research was supported by the National Key R&D Program of China (2016YFA0602601), Special Items Fund of Beijing Municipal Commission of Education of China, Program of Beijing Energy Development Research Center of China (NYJD20170101), and National Social Science Fund of China (15ZDA011).

References

  1. Alves MR, Moutinho V (2013) Decomposition analysis and innovative accounting approach for energy-related CO2, (carbon dioxide) emissions intensity over 1996–2009 in Portugal. Energy 57(3):775–787CrossRefGoogle Scholar
  2. Ang BW, Su B (2016) Carbon emission intensity in electricity production: a global analysis. Energy Policy 94:56–63CrossRefGoogle Scholar
  3. Anselin L (1987) Spatial econometrics: methods and models. Econ Geogr 65(2):160–162Google Scholar
  4. Anselin L (2001) Spatial effects in econometric practice in environmental and resource economics. Am J Agric Econ 83(3):705–710CrossRefGoogle Scholar
  5. Anselin L (2007) Spatial econometrics. A companion to theoretical econometrics. Blackwell Publishing Ltd, Hoboken, pp 310–330Google Scholar
  6. Baltagi BH (2013) Econometric analysis of panel data, 4th edition. Econ Theor (5):747–754Google Scholar
  7. Barnali Nag M (2000) Carbon emission intensity of power consumption in India: a detailed study of its indicators. Energy Sources 22(2):157–166CrossRefGoogle Scholar
  8. Bhattacharyya SC, Matsumura W (2010) Changes in the GHG emission intensity in eu-15: lessons from a decomposition analysis. Energy 35(8):3315–3322CrossRefGoogle Scholar
  9. Chen S (2011) The abatement of carbon dioxide intensity in China: factors decomposition and policy implications. World Econ 34(7):1148–1167CrossRefGoogle Scholar
  10. Chen Y, Lin S (2015) Decomposition and allocation of energy-related carbon dioxide emission allowance over provinces of China. Nat Hazards 76(3):1893–1909CrossRefGoogle Scholar
  11. Chik NA, Rahim KA, Radam A, Shamsudin MN (2013) Impact of Malaysian industrial energy use on carbon dioxide emissions. Pertan J Soc Sci Humanit 21:13–28Google Scholar
  12. Cui E, Ren L, Sun H (2016) Analysis of energy-related CO2 emissions and driving factors in five major energy consumption sectors in China. Environ Sci Pollut Res Int 23(19):1–8CrossRefGoogle Scholar
  13. Dong F, Long R, Li Z, Dai Y (2016) Analysis of carbon emission intensity, urbanization and energy mix: evidence from China. Nat Hazards 82(2):1375–1391CrossRefGoogle Scholar
  14. Du G (2013) Analysis of carbon emission based on stochastic IPAT model. Int J Appl Math Stat™ 47(17):230–238Google Scholar
  15. Ebohon OJ, Ikeme AJ (2006) Decomposition analysis of CO2, emission intensity between oil-producing and non-oil-producing sub-Saharan African countries. Energy Policy 34(18):3599–3611CrossRefGoogle Scholar
  16. Elhorst JP (2005) Unconditional maximum likelihood estimation of linear and log-linear dynamic models for spatial panels. Geogr Anal 37(1):85–106CrossRefGoogle Scholar
  17. Elhorst JP (2016) Specification and estimation of spatial panel data models. Int Reg Sci Rev 26(3):244–268CrossRefGoogle Scholar
  18. Fan F, Lei Y (2016) Decomposition analysis of energy-related carbon emissions from the transportation sector in Beijing. Transp Res Part D 42:135–145CrossRefGoogle Scholar
  19. Fan JL, Liao H, Liang QM, Tatano H, Liu CF, Wei YM (2013) Residential carbon emission evolutions in urban–rural divided China: an end-use and behavior analysis. Appl Energy 101(1):323–332CrossRefGoogle Scholar
  20. Fang G, Tian L, Fu M, Sun M (2013) The impacts of carbon tax on energy intensity and economic growth—a dynamic evolution analysis on the case of China. Appl Energy 110(5):17–28CrossRefGoogle Scholar
  21. Fisher-Vanden K, Schu K, Wing IS, Calvin K (2012) Decomposing the impact of alternative technology sets on future carbon emissions growth. Energy Econ 34(2):S359–S365CrossRefGoogle Scholar
  22. Giblin S, Mcnabola A (2009) Modelling the impacts of a carbon emission-differentiated vehicle tax system on co emissions intensity from new vehicle purchases in Ireland. Energy Policy 37(4):1404–1411CrossRefGoogle Scholar
  23. Greening LA (2004) Effects of human behavior on aggregate carbon emission intensity of personal transportation: comparison of 10 OECD countries for the period 1970–1993. Energy Econ 26(1):1–30CrossRefGoogle Scholar
  24. Greening LA, Davis WB, Schipper L (1998) Decomposition of aggregate carbon emission intensity for the manufacturing sector: comparison of declining trends from 10 OECD countries for the period 1971–1991. Energy Econ 20(97):43–65CrossRefGoogle Scholar
  25. Greening LA, Ting M, Davis WB (1999) Decomposition of aggregate carbon emission intensity for freight: trends from 10 OECD countries for the period 1971–1993. Energy Econ 21(4):331–361CrossRefGoogle Scholar
  26. Greening LA, Ting M, Krackler TJ (2001) Effects of changes in residential end-uses and behavior on aggregate carbon emission intensity: comparison of 10 OECD countries for the period 1970 through 1993. Energy Econ 23(2):153–178CrossRefGoogle Scholar
  27. Hammond GP, Norman JB (2012) Decomposition analysis of energy-related carbon emissions from UK manufacturing. Energy 41(1):220–227CrossRefGoogle Scholar
  28. Jiang J (2017) The decomposition and policy meaning of China’s carbon emission intensity. Evolut Inst Econ Rev 14(1):295–310CrossRefGoogle Scholar
  29. Jiao J, Yang Y, Bai Y (2018) The impact of inter-industry R&D technology spillover on carbon emission in China. Nat Hazards 91(3):913–929CrossRefGoogle Scholar
  30. Jovanović M, Kašćelan L, Despotović A, Kašćelan V (2015) The impact of agro-economic factors on GHG emissions: evidence from european developing and advanced economies. Sustainability 7(12):16290–16310CrossRefGoogle Scholar
  31. Köne AÇ, Büke T (2016) The impact of changing energy mix of turkey on CO2 emission intensities. Environ Prot Eng 42(3):85–93Google Scholar
  32. Lee LF, Yu J (2010) Estimation of spatial autoregressive panel data models with fixed effects. J Econ 154(2):165–185CrossRefGoogle Scholar
  33. Lessage JP, Pace RK (2009) Introduction to spatial econometrics. CRC Press, Boca Raton. Spatial Demography, 1(1):143–145Google Scholar
  34. Li ZZ (2013) Mining and analyzing energy layout on carbon emission intensities of industrial sectors. Adv Mater Res 807–809:857–860Google Scholar
  35. Li W, Ou QX (2013) Decomposition of China’s carbon emissions intensity from 1995 to 2010: an extended KAYA identity. Math Probl Eng 2013(3):1–7Google Scholar
  36. Li M, Wang Q (2017) Will technology advances alleviate climate change? Dual effects of technology change on aggregate carbon dioxide emissions. Energy Sustain Dev 41:61–68CrossRefGoogle Scholar
  37. Lin B, Nelson BI (2017) Influencing factors on carbon emissions in China transport industry. a new evidence from quantile regression analysis. J Clean Prod 150:175–187CrossRefGoogle Scholar
  38. Lin B, Omoju OE, Okonkwo JU (2015) Impact of industrialization on CO2 emissions in Nigeria. Renew Sustain Energy Rev 52:1228–1239CrossRefGoogle Scholar
  39. Liu SB (2013) Energy consumption and structural reformation in Chinese northeast old industrial base. Appl Mech Mater 448–453:4281–4284CrossRefGoogle Scholar
  40. Liu N, Ma Z, Kang J (2015) Changes in carbon emission intensity in China’s industrial sector: decomposition and attribution analysis. Energy Policy 87:28–38CrossRefGoogle Scholar
  41. Long R, Yang R, Song M, Ma L (2015) Measurement and calculation of carbon emission intensity based on impact model and scenario analysis: a case of three regions of Jiangsu province. Ecol Ind 51(2):180–190CrossRefGoogle Scholar
  42. Lu IJ, Lin SJ, Lewis C (2007) Decomposition and decoupling effects of carbon dioxide emission from highway transportation in Taiwan, Germany, Japan and south Korea. Energy Policy 35(6):3226–3235CrossRefGoogle Scholar
  43. Moutinho V, Robaina-Alves M, Mota J (2014) Carbon dioxide emissions intensity of Portuguese industry and energy sectors: a convergence analysis and econometric approach. Renew Sustain Energy Rev 40(C):438–449CrossRefGoogle Scholar
  44. Nag B, Parikh J (2000) Indicators of carbon emission intensity from commercial energy use in India. Energy Econ 22(4):441–461CrossRefGoogle Scholar
  45. Ohlan R (2015) The impact of population density, energy consumption, economic growth and trade openness on CO2, emissions in India. Nat Hazards 79(2):1–20CrossRefGoogle Scholar
  46. Piras G, Elhorst JP, Arbia G (2010) Growth and convergence in a multiregional model with space–time dynamics. Geogr Anal 42(3):338–355CrossRefGoogle Scholar
  47. Ren S, Yuan B, Ma X, Chen X (2014) The impact of international trade on China’s industrial carbon emissions since its entry into WTO. Energy Policy 69(3):624–634CrossRefGoogle Scholar
  48. Sambodo MT, Oyama T (2011) Investigating economic growth, energy consumption and their impact on CO2 emissions targets in China. J Asian Public Policy 4(3):279–306CrossRefGoogle Scholar
  49. Su B, Ang BW (2016) Multi-region comparisons of emission performance: the structural decomposition analysis approach. Ecol Ind 67:78–87CrossRefGoogle Scholar
  50. Sun C, Ding D, Yang M (2017) Estimating the complete CO2 emissions and the carbon emission intensity in India: from the carbon transfer perspective. Energy Policy 109:418–427CrossRefGoogle Scholar
  51. Tian Y, Xiong S, Ma X, Ji J (2018) Structural path decomposition of carbon emission: a study of China’s manufacturing industry. J Clean Prod 61(480):113–121Google Scholar
  52. Torvanger A (1991) Manufacturing sector carbon dioxide emissions in nine OECD countries, 1973–87: a divisia index decomposition to changes in fuel mix, emission coefficients, industry structure, energy intensities and international structure. Energy Econ 13(3):168–186CrossRefGoogle Scholar
  53. Wang J, Ren Y (2013) Dynamic impact of economic development mode transformation on CO2 emission intensity reduction. Int J Appl Environ Sci 8(21):2665–2677Google Scholar
  54. Wang P, Dai HC, Ren SY, Zhao DQ, Masui T (2015) Achieving Copenhagen target through carbon emission trading: economic impacts assessment in Guangdong province of China. Energy 79(79):212–227CrossRefGoogle Scholar
  55. Wei P, Zhou Y (2014) Urbanization, energy consumption and carbon emission: a empirical study on transnational panel data based on STIPAT model. Ecol Econ 14(2):571–575Google Scholar
  56. Wen L, Bai L, Zhang E (2016) System dynamic modeling and scenario simulation on Beijing industrial carbon emissions. Environ Eng Res 21(4):355–364CrossRefGoogle Scholar
  57. Xu F, Xiang N, Nijkamp P, Higano Y (2013) Dynamic simulation of China’s carbon emission intensity and energy intensity evaluation focusing on industry and energy structure adjustments by 2020. Environ Eng Manag J 12(10):1897–1901CrossRefGoogle Scholar
  58. Yu X, Chen H, Wang B, Wang R, Shan Y (2018) Driving forces of CO2 emissions and mitigation strategies of China’s national low carbon pilot industrial parks. Appl Energy 212:1553–1562CrossRefGoogle Scholar
  59. Zhang YJ, Da YB (2015) The decomposition of energy-related carbon emission and its decoupling with economic growth in China. Renew Sustain Energy Rev 41:1255–1266CrossRefGoogle Scholar
  60. Zhang YJ, Liu Z, Zhang H, Tan TD (2014) The impact of economic growth, industrial structure and urbanization on carbon emission intensity in China. Nat Hazards 73(2):579–595CrossRefGoogle Scholar
  61. Zhang W, Li K, Zhou D, Zhang W, Gao H (2016) Decomposition of intensity of energy-related CO2, emission in Chinese provinces using the LMDI method. Energy Policy 92:369–381CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Economic Forecasting DepartmentState Information CenterBeijingChina
  2. 2.Beijing Energy Development Research CenterBeijingChina
  3. 3.State Information CenterBeijingChina
  4. 4.School of Environment and Natural ResourcesRenmin University of ChinaBeijingChina

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