Carbon Emission Performance Research Articles

Regional difference and driving factors of industrial carbon emissions performance in China

Industry is the pillar of China’s national economy. The energy-saving and emission reduction in the industrial sector directly bear on the sustainable development of the whole country. This paper sets up an evaluation index system for industrial carbon emissions performance (ICEP), and then evaluates the ICEPs in 30 Chinese provinces from 2003 to 2017, using the stochastic block model (SBM). Based on the regional difference of the ICEP, a Tobit model was established to identify the driving factors of the ICEP. The main results are as follows: (1) There are obvious provincial differences in the ICEP: coastal provinces (e.g. Zhejiang, Shanghai, Beijing, Jiangsu, and Guangdong) had relatively high ICEPs, while inland provinces (e.g. Yunnan, Guizhou, Ningxia, Shanxi and Qinghai) had undesirable ICEPs. (2) The three regions of China can be ranked as eastern region, central region, and western region in descending order of the ICEP. The central and western regions should be focused to promote energy-saving and emission reduction in the industrial sector. (3) The ICEP clustering shows that the ICEPs were medium and low in most provinces, and high in very few provinces. (4) Regression results of the Tobit model demonstrate that different driving factors have varied effects on the ICEP: economic development, technical progress, and foreign trade have significant positive effects on the ICEP, while energy structure and environmental regulation have significant negative effects on the ICEP.

Carbon emissions performance trend across Chinese cities: evidence from efficiency and convergence evaluation.

Improving carbon emissions performance in Chinese cities is a crucial way to promote China's sustainable development. Employing the super-efficiency SBM model, we first estimate the carbon emissions efficiency (CEE) of 262 Chinese cities from 2003 to 2016. Then we study and explain the club convergence of CEE combining Markov and spatial Markov models and Moran's I test method. The results show that CEE has improved, especially for the western and northeastern cities. The efficiency of the northwest cities is low, while those of the central and coastal cities are relatively high. Club convergence exists in China's urban CEE. Cities with high- and low-level efficiency have much higher convergence levels. There are significant spatial agglomeration and spillover effects in China's urban CEE, contributing to the club convergence. Our analysis suggests that "cross-border" cooperation and communication between cities in different clubs should be highly promoted. Cities in high-level efficiency clubs are encouraged to play its role in radiating the lower-level cities. And the Chinese government is encouraged to strengthen carbon emissions mitigation in low-level areas through combining the green "Belt and Road" construction with the establishment of a national carbon market.

Carbon mitigation by the construction industry in China: a perspective of efficiency and costs.

Evaluating carbon emission performance of the construction industry is a significant prerequisite for developing regional carbon mitigation plans. Taking environmental and technical heterogeneities into account, this paper employed a meta-frontier method to measure the carbon emission efficiency, carbon mitigation potential, and costs of the construction sector in different regions of China from 2005 to 2016. The empirical results show that substantial disparities in carbon emission efficiency exist in the construction industry. The total carbon mitigation potential of this sector was 206.76 million tons, with the Lower Yellow river area accounting for the largest proportion at 27%. Meanwhile, the carbon mitigation costs of this sector increased from 584.94 to 1273.30 yuan/ton during 2005-2016. The highest mitigation costs occur in the Lower Yangtze River area and the South Coastal area, indicating it was more costly in these areas to conduct additional carbon emissions mitigation. The results could facilitate the policy formulation on regional-oriented carbon emissions mitigation of the construction industry in China.

Heterogeneity in the relationship between carbon emission performance and urbanization: evidence from China.

Global change caused by carbon emissions alone has become a common challenge for all countries. However, current debates about urbanization and carbon emissions generally do not take into account the heterogeneities in urbanization and economic development levels. The goal of this study is to revisit the urbanization–emissions nexus by considering such heterogeneities in the Chinese context. The results reveal that there is significant heterogeneity in the total factor carbon emission performance index across provinces. Specifically, the relationship between carbon emission performance and urbanization reflects a U-shaped curve. Urbanization is found to have a stronger inhibiting effect on carbon emission performance when economic development levels improve. The results suggest that tailoring policies to each region’s conditions, promoting investments in energy-saving and emissions-reducing technologies, and improving the use of public transportation could be mitigation strategies for global change that lead to low-carbon urbanization.

Upgrading or downgrading: China's regional carbon emission intensity evolution and its determinants

One of China's major national targets is to environmentally upgrade its economy. In this paper, we define environmental upgrading as lowering the carbon intensity. The disparities among China's regions suggest to examine China's carbon emission performance at the regional level. For this purpose, we use inter-regional input-output tables (for 2002, 2007, and 2012) that distinguish processing exports from ordinary exports. The regional emission intensities (EIs) show environmental downgrading in the period 2002–2007 and upgrading during 2007–2012. To identify the determinants of the evolution of regional EIs, we have employed a multiplicative structural decomposition analysis. Changes in direct emission coefficients and changes in production technology are found to be the major determinants. However, next to these standard determinants, we also evaluate the effects on the changes in regional EIs of changes in inter-regional trade and changes in inter-regional spillovers. Changing inter-regional trade is found to have increased the EI significantly in western and central regions. This suggests that more “dirty” production was shifted from coastal to inland regions. Our study yields clear policy recommendations for achieving China's transformation to a low-carbon economy. • China's regional carbon emission performance experienced “downgrading” first and then “upgrading” during 2002–2012. • There are large regional disparities in the emission intensity (EI) evolution. • China's inter-regional input-output tables that distinguish processing exports are adopted. • Multiplicative structural decomposition analysis (SDA) is used to identify the determinants of China's regional EI changes. • Shifts of “dirty” production from coastal to inland regions have increased the EIs in inland regions.

Time Series Analysis for the Dynamic Relationship between an Enterprise’s Business Growth and Carbon Emission in Taiwan

Since the Paris Agreement came into effect in 2016, governments worldwide have established goals for future carbon emission reduction. Enterprises and governments have begun to pay attention to the management of carbon emission. This study explored the dynamic relationship between business growth and carbon emission performance by constructing and using a time series model to predict the trend of carbon emission. The time series method (ADF unit root test, cointegration test and VAR model etc.) was adopted to investigate 805 companies listed on the Taiwan Stock Exchange from 2012 to 2017 as the sample of this study. The carbon emission performance variables include: total carbon emission, annual increase of carbon emission, and annual increase rate of carbon emission. The business growth variables include: growth rate, stability of growth, and years of establishment. The results showed a long-term dynamic relationship between business growth and carbon emission performance. Therefore, using the time series method can assist enterprises in developing green strategies, strengthen carbon emission prediction and management capacities, reduce operating costs and risks, and actively achieve the ideal of sustainable development.

An Efficiency Perspective on Carbon Emissions and Financial Performance

International policy actions to constrain carbon emissions create substantial risks and opportunities for firms. In particular, production processes that are relatively high emitting will be more sensitive to the uncertain costs of emitting carbon dioxide and might further reflect productive inefficiencies. We employ a productive efficiency model to evaluate firms' carbon emission levels relative to those of best-practice (efficient) peers with comparable production structures. By accounting for total factor productivity and sector-relative performance aspects, this measure of carbon efficiency helps to quantify and rank firms' relative dependence on carbon in the production process. We investigate the impact of carbon efficiency on various financial performance outcomes and evaluate the role of general resource efficiency in explaining these impacts. Using an international sample of 1572 firms over the years 2009–2017, we find superior financial performance in carbon-efficient (best-practice) firms. On average, a 0.1 higher carbon efficiency is associated with a 1.0% higher profitability and 0.6% lower systematic risk. While carbon efficiency closely relates to resource efficiency, it also has distinct financial performance impacts, particularly lowering systematic risk. Overall, our findings suggest that carbon-efficient production can be valuable from both operational and risk management perspectives.

Spatiotemporal evolution of urban carbon emission performance in China and prediction of future trends

Climate change resulting from CO2 emissions has become an important global environmental issue in recent years. Improving carbon emission performance is one way to reduce carbon emissions. Although carbon emission performance has been discussed at the national and industrial levels, city-level studies are lacking due to the limited availability of statistics on energy consumption. In this study, based on city-level remote sensing data on carbon emissions in China from 1992–2013, we used the slacks-based measure of super-efficiency to evaluate urban carbon emission performance. The traditional Markov probability transfer matrix and spatial Markov probability transfer matrix were constructed to explore the spatiotemporal evolution of urban carbon emission performance in China for the first time and predict long-term trends in carbon emission performance. The results show that urban carbon emission performance in China steadily increased during the study period with some fluctuations. However, the overall level of carbon emission performance remains low, indicating great potential for improvements in energy conservation and emission reduction. The spatial pattern of urban carbon emission performance in China can be described as “high in the south and low in the north,” and significant differences in carbon emission performance were found between cities. The spatial Markov probabilistic transfer matrix results indicate that the transfer of carbon emission performance in Chinese cities is stable, resulting in a “club convergence” phenomenon. Furthermore, neighborhood backgrounds play an important role in the transfer between carbon emission performance types. Based on the prediction of long-term trends in carbon emission performance, carbon emission performance is expected to improve gradually over time. Therefore, China should continue to strengthen research and development aimed at improving urban carbon emission performance and achieving the national energy conservation and emission reduction goals. Meanwhile, neighboring cities with different neighborhood backgrounds should pursue cooperative economic strategies that balance economic growth, energy conservation, and emission reductions to realize low-carbon construction and sustainable development.

Study on industrial selection of counterpart cooperation between Jilin province and Zhejiang province in China from the perspective of low carbon.

Counterpart cooperation is a major innovative measure in China's strategy for revitalizing north-eastern China. While promoting economic progress, regional counterpart cooperation should also focus on low-carbon economy and sustainable development. Under the background of China's proposed innovative cooperation strategy, using a multi-regional input-output (MRIO) model and structural decomposition analysis (SDA), this study takes Jilin province and its counterpart Zhejiang province as an example and decomposes the change in carbon emission intensity (CEI), which is a widely used indicator to measure regional carbon emission performance. The decomposition spans the years 2007 to 2012, at the level of two provinces and departments. By comparing the factors that drive and inhibit CEI in the two provinces, it was found that the production technology effect in Jilin province primarily drove the growth in CEI, while in Zhejiang province, the opposite occurred. Second, the structural effects of agriculture and heavy industry in Jilin province accounted for the largest proportion of this change, and the pulling effect on the increase in CEI in Jilin was significantly higher than that in Zhejiang province. Third, the scale effect of agricultural demand in Zhejiang province was much higher than that in Jilin province, and the same trend was observed for the scale effect of heavy industrial exports.

Modelling energy and carbon emission performance: A constrained performance index measure

Energy and carbon emission performance evaluation is a focus topic in the field of environmental protection and energy utilization. However, the current studies are not high in identifying economic output inefficiency and neglect to control target energy carbon emission intensity (ECI), which may cause evaluation bias. Hence, this paper presents a constrained performance index measure (CPIM) model to solve the issues. The proposed model takes the performance index measure and can identify more numbers of positive economic output inefficiency compared to the directional distance function (DDF) model. Besides, the ECI constraint is presented and imposed into the proposed model to restrict the target ECI of decision making units. The numerical example from China’s provincial dataset demonstrates and confirms the validity and merit of the proposed model through the measure and constraint comparison among different models. Then, it is found that most provinces exist certain economic output inefficiency and the center regions have a worst score of energy carbon emission performance index due to the rapid development of their secondary industry by using the proposed model. Several valuable modeling directions and questions related to performance index measure and energy pollution emission constraints are provided for future research.

Carbon emission performance and quota allocation in the Bohai Rim Economic Circle

As the most developed economic zone in northern China, the establishment of regional carbon emission trading (CET) market in the Bohai Rim Economic Circle (BREC) is vital to achieve the Chinese government’s carbon emissions reduction target. This study estimates the carbon emissions in the BREC from 2005 to 2017. To evaluate the impact of energy consumption structure on carbon emission performance in this region, a directional distance function is constructed, and two scenarios are assumed. Then, a two-stage allocation model is established to allocate the carbon emission quotas in 2030. The results indicate the following: (1) Carbon emission intensity tends to decrease and there are large differences among provinces. (2) Energy consumption structure has a great effect on carbon emission performance, ignoring energy consumption structure will underestimate carbon emission performance. (3) Total accumulated carbon emission targets are 85.20 billion tons, 82.36 billion tons and 79.62 billion tons at the high, medium and low economic growth rates. These results provide a reference for the regional carbon emission quotas allocation and for accelerating the establishment of a national CET market.

Evaluation of regional metafrontier total factor carbon emission performance in China’s construction industry: Analysis based on modified non-radial directional distance function

We establish a metafrontier total factor carbon emission performance index (NMTCPI) of 30 provinces’ construction industry in China during 2004–2017. In order to avoid the problem of two efficiency decomposition terms which includes the best practice gap ratio and technical gap ratio greater than 1, the non-radial directional distance function is modified. The results are concluded as follows: (1) Carbon emissions efficiency in China’s construction industry is at low level and grows rapidly during 2004–2017, the potential for CO2 emission reduction is huge. (2) Decomposition analysis reveals that the increase of NMTCPI principally attributed to technical progress. Technical efficiency has gone through a process of first decline and then rise, which means that the construction industry has changed from extensive development to a low-carbon development model. However, the regional technology gap continues to widen after 2011. (3) Different regions have different driving forces. The improvement of carbon emission efficiency in the eastern region comes from the narrowing technical gap and technical progress, while the increase of carbon emission efficiency in the central and western regions is due to rapid technical progress. For most provinces in western group, the sharp decline in technical efficiency hinders the growth of carbon emission efficiency.

The Assessment of Carbon Performance under the Region-Sector Perspective based on the Nonparametric Estimation: A Case Study of the Northern Province in China

China is the largest emitter of carbon dioxide (CO2) in the world, and the Chinese government has accordingly proposed a series of measures to achieve a low-carbon economy. Due to the low carbon emission performance (CEP) and the high industry portion of the northern provinces in China, evaluating the CEPs of industrial sectors in northern China is necessary. By considering the different CEP assessments in regional and industrial research, a dual-perspective assessment of CEP was presented to narrow the gap between the regional and industrial perspectives. The dual model of slack-based measure (SBM) and data envelopment analysis (DEA) was combined with the global Malmquist–Luenberger index (GMLI) proposed to measure the static CEP and the dynamic change of the CEP of six provinces in northern China from 2006–15 for the regional and industrial perspectives, respectively. A comparison of the results under the different perspectives proved the irrationality of our evaluation under the sole perspective. For example, for Jilin Province, the CEPs of Mining and Processing of Nonmetal and Other Ores (Sector 4) ranked in the top 30% in the regional perspective. However, in the industrial level, the CEPs of Mining and Processing of Nonmetal and Other Ores (Sector 4) ranked lower. The CEPs of the Production and Supply of Electric Power and Heat Power (Sector 20) of Heilongjiang Province ranked in the bottom 30% in a regional perspective but ranked first at the industrial level. We also found the advantage sectors in the CEP under the region–sector dual perspective. For example, for Jilin Province, the Processing of Petroleum, Coking, and Processing of Nuclear Fuel (Sector 10) and the manufacture of Transport Equipment (Sector 16) were the advantageous sectors. The dual-perspective assessment aimed to evaluate the CEP under diverse views. It also provided a more reliable path to reduce CO2 emissions for managers and regulators.

China’s aggregate embodied CO2 emission intensity from 2007 to 2012: A multi-region multiplicative structural decomposition analysis

The striking disparities among China’s provinces suggest the need for regionalized carbon emission intensity mitigation measures. This study extends the aggregate embodied energy/emission intensity measures proposed by Su and Ang (2017) to a national multi-region setting, defines the aggregate embodied CO2 emission intensity (AECI) indicators at the aggregate, provincial, and final demand category levels from the demand perspective, and adopts multiplicative structural decomposition analysis (SDA) to identify the driving factors of the historical changes over the 2007–2012 period in China. On the basis of China’s latest multiregion input-output tables, we find that (a) the AECIs show heterogeneities across the provinces and final demand categories, with the AECIs of inland provinces higher than those of coastal provinces and the AECIs of investment and export higher than those of consumption; (b) the generally downward trends of the AECIs hold at different levels, and the improvements in carbon emission performances are more pronounced in inland provinces, presenting a convergence toward the levels of developed coastal provinces; (c) according to the multiplicative SDA results, the input structure effect and the emission intensity effect generally contribute to the decline of AECIs. By providing a deeper understanding of carbon emission performance and its driving factors at the subnational level, our study finally proposes some policy recommendations.

Comparisons of CO2 emission performance between secondary and service industries in Yangtze River Delta cities

To put the brakes on global climate change, China, the world's top emitter, has established ambitious CO2 emissions reduction targets. Industry-level emissions analysis can help policymakers determine better ways to achieve mitigation targets. This study is the first to target the total-factor carbon emission performance (TCPI) of secondary and service industries. We first compile industry-level CO2 emission inventories of 25 Yangtze River Delta cities during 2007–2016. The TCPI of secondary and service industries is then estimated by the non-radial directional distance function. We then compare the TCPI of the two industries across levels, dynamics, and inequalities using a global metafrontier approach. The results show the TCPI of the service industry (0.563 in 2016) was significantly higher than that of secondary industry (0.256 in 2016), suggesting that the service industry was more carbon-friendly. The TCPI gap between the secondary and service industries narrowed over the study period. The TCPI of secondary industry showed a promising increase during 2007–2016 with an annual growth rate of 2.30%, reflecting the positive effects of the government's reforms and environmental regulations. By contrast, the service industry saw a downward trend in TCPI, decreasing by 1.68% annually, primarily because it is a newcomer to low-carbon development. TCPI inequality in secondary industry was much larger than in the service industry, suggesting that significant heterogeneity exists in secondary industry. Therefore, policymakers should implement targeted mitigation policies for secondary industry, and place decarbonising the service industry on the agenda to reverse its decreasing TCPI.

Investigation of a spatial coupling relationship between carbon emission performance and regional urbanization in China.

In light of the problem of environmental pollution caused by fossil fuel combustion, and its association with rapid urbanization, China is grappling with the question of how to reduce carbon emissions through more efficient energy consumption while simultaneously advancing its economic development. We applied a directional distance function to estimate the carbon emission performance of 30 provinces in China during the period 2000–2016. We selected an index system to assess urbanization processes in these provinces and conducted a spatial analysis to investigate the relationship between urbanization and carbon emission performance. We obtained the following results. First, the carbon emission performance of the eastern region, valued at 0.853, was relatively higher than the corresponding values of 0.810, 0.804, and 0.843 in the central, western, and northeastern regions, respectively. However, during this period, disparities among provinces increased. Second, the average urbanization value for each province showed an upward trend during the study period, and urbanization assumed a “striped” spatial agglomeration pattern. A third finding was that carbon emission performance and urbanization demonstrated a relationship of positive spatial dependence. The average value of their coordinated coupling indicated an upward trend, with an annual increase of 0.85%. Last, we found that efforts to reduce carbon emissions that are solely based on carbon emission performance do not yield reliable results. Accordingly, measurements of urbanization values can enable more detailed differentiation. In conclusion, reasonable measures should be implemented to improve carbon emission performance and urbanization that are in alignment with the actual situation within a given region.

Impact of prefabrication technology on the cradle-to-site CO2 emissions of residential buildings

In recent years, the industrialization of construction has been promoted to achieve sustainable development within the construction industry; accordingly, prefabrication technology has been utilized substantially. Due to enormous carbon emission reduction pressure, the carbon-reducing potential of prefabrication has attracted widespread interest. To deepen the current understanding of the carbon emission performance of prefabrication, this study investigates the differences in CO2 emissions between the prefabricated and conventional construction methods and the effect of the prefabrication rate on building carbon emissions. A quantitative model is established based on life-cycle assessment (LCA) to calculate the cradle-to-site CO2 emissions of the two construction methods. The LCA model is then parameterized to simulate the cradle-to-site CO2 emissions at varying prefabrication rates. The simulation is conducted based on three scenarios, i.e., some slabs and staircases are prefabricated, all transverse members are prefabricated, and all components are prefabricated. Two sample buildings in China, where housing industrialization has developed rapidly, are used for a preliminary examination. The results show that the conventional building produces 185.13 kgCO2/m2, whereas the prefabricated building produces 151.84 kgCO2/m2, which is approximately 18% lower than the former value. Additionally, the CO2 emissions do not necessarily decrease with an increase in the prefabrication rate. The building with some prefabricated slabs and staircases produces the least CO2 emissions, and the building with the highest prefabrication rate still produces less CO2 emissions than the conventional building. Moreover, as the prefabrication rate increases, the carbon emissions during the construction cycle present a shift towards manufacturing and transportation. Therefore, adopting prefabrication technology contributes to significant environmental benefits for reducing CO2 emissions; the optimal prefabrication rate can be chosen according to the demand. Carbon reduction in manufacturing and transportation should be a new focus.

Measuring carbon emission performance of industrial sectors in the Beijing–Tianjin–Hebei region, China: A stochastic frontier approach

Carbon emissions have become a global environmental problem in recent years. It is necessary to explore methods to improve the efficiency of energy consumption and minimize carbon emissions that accompany economic development and rapid urbanization. In this paper, we quantitatively measure the total factor carbon emission performance (TFCP) and the carbon emission mitigation potential (CMP) of 39 industrial sectors in the Beijing–Tianjin–Hebei (BTH) region from 2010 to 2016 by employing a stochastic frontier approach. The results show that the average TFCP value of the 39 industrial sectors in the BTH region was relatively low (0.7733). From 2010 to 2016, the TFCP increased in Beijing, decreased in Tianjin and held steady in Hebei. The entire level of TFCP in Hebei was the greatest, followed by Tianjin and Beijing. The CMP in the BTH region increased slightly during the period 2010–2016. The total CMP of the 39 industrial sectors in the BTH region was 11.03 × 108 t from 2010 to 2016. The industrial sectors were divided into four categories according to the relationship between TFCP and CMP. Belonging to the low TFCP–high CMP quadrant, the Manufacture of Nonmetallic Mineral Products sector and the Production and Distribution of Electric Power and Heat Power sector should attract more attention when formulating carbon reduction policies and related regulations. According to the results, we also present suggestions to strengthen green economic growth and regional sustainable development.

Carbon emissions performance in logistics at the city level

The importance of good performance in the logistics industry contributing towards a low-carbon economy is widely recognised. However, there are few studies on carbon emissions performance for the logistics industry, especially at the city level. Therefore, this study attempts to analyse the carbon emissions performance of the logistics industry at the city level by examining sixteen cities within Yunnan Province. In particular, the Data envelopment analysis (DEA) model and Malmquist index from both the static and dynamic perspective were explored. To further capture the driving factors of the carbon emissions performance in logistics, the Tobit model was applied to perform regression analysis. The results indicate that (1) with regards to static performance, two northwest cities, Nujiang and Diqing, have reached the technological frontier; (2) regarding dynamic performance, although six cities have improved, the average dynamic logistics carbon emissions performance of all the cities decreases by approximately 5.9% from 2011 to 2015; (3) in terms of driving factors, the economic development factor is found to be positively and significantly related to static carbon emissions performance, whereas others indicate negative and significant relationships.

Understanding the trend of total factor carbon productivity in the world: Insights from convergence analysis

Improving carbon emission performance is widely regarded as one of the most cost-effective ways to combat clime change. This paper employs a parametric Malmquist index approach to measure total factor carbon productivity in 88 economies over the period of 1975–2013. Then, we explore the trend of carbon productivity with the log t regression test and identify potential convergent clubs. Furthermore, the Ordered Probit model is employed to examine the factors affecting the club convergence of carbon productivity growth. The main findings are as follows: (1) the carbon productivity on average increased by 63.5% during the sample period, mainly driven by technological progress; (2) the developed countries performed better than the developing countries. Meanwhile, the developed economies were far ahead in terms of technological progress, while the developing ones have performed slightly better in terms of carbon efficiency improvement; (3) although the whole sample did not show convergent trend, five convergence clubs formed with significant differences in carbon productivity growth; (4) the economies with higher GDP per capita and R&D investment tended to converge to the club with a higher carbon productivity, while the economies with higher energy intensity and foreign trade dependence tended to converge to the club with a lower carbon productivity. Finally, we draw some important policy implications.