Probing the affecting factors and decoupling analysis of energy industrial carbon emissions in Liaoning, China

Low-carbon economic growth in Chinese cities: a case study in Shenzhen city

Low-carbon economic growth in cities is important for reduction of carbon emissions in China. As the best practice city in China, Shenzhen city has experienced rapid economic growth with low carbon emissions. The study aims to evaluate the performance of Chinese cities on low-carbon economic growth through the case study of Shenzhen city. The study carries out the Tapio decoupling model for analyzing decoupling state, and uses the Kaya-Logarithmic Mean Divisia Index decomposition model to determine the main driving factors of carbon emissions in Shenzhen. Results indicate that Shenzhen has greatly decoupled carbon emissions with economic growth. The analysis of driving factors of carbon emission shows that the declining energy intensity and the upgrading industrial structure effectively hamper the increase of carbon emissions in Shenzhen. The decline in energy intensity in Shenzhen may come from an improvement of production efficiency of the industries. However, the irrational energy consumption structure, fast-growing economic output, and industry scale are hampering the low carbon emissions of Shenzhen. All estimated industries are highly dependent on coal and oil although some industries have slightly increased their proportion of clean energy consumption. Pursuing more clean energy consumption in the industry will be a key development strategy for reducing emissions in the future. Moreover, as Shenzhen is a fast-growing city, the increasing economic output and industry scale are inevitable. Changing people's way of living could also help in reducing carbon emissions in cities.

Influencing factors and decoupling analysis of carbon emissions in China's manufacturing industry

The manufacturing industry directly reflects national productivity, and it is also an industry with high energy consumption and severe carbon emissions. This study decomposes the influential factors on carbon emissions in China's manufacturing industry from 1995 to 2018 into industry value added, energy consumption, fixed asset investment, carbon productivity, energy structure, energy intensity, investment carbon intensity, and investment efficiency by Generalized Divisia Index Model. The decoupling analysis of carbon emissions and industry value added is carried out to investigate the states of the manufacturing industry under the pressure of "low carbon" and "economy." Results show that first, fixed asset investment is the driving force of carbon emissions, followed by industry value added; investment carbon intensity, carbon productivity, investment efficiency, and energy intensity are the mitigating factors; simultaneously, the impacts of energy consumption and energy structure are fluctuating. Second, the decoupling of manufacturing has improved, especially in the light industry. Third, the decoupling of carbon emissions and economic development is mainly dominated by the decoupling of energy consumption and industry added value. Therefore, reducing the proportion of coal consumption and optimizing the energy structure are significant ways to promote the low-carbon development of the manufacturing industry.

The impact of innovation on environmental quality: Evidence for the non-linear relationship of patents and CO2 emissions in China

We seek to test whether innovation, measured by the number of accepted patents improves or worsens the environment in China. We hypothesize the existence of an inverse U-shaped curve, which differs by industry and provincial level of development. In that context, we test for a quadratic relationship between accepted patents and CO2 emissions per capita in 30 provinces and 32 economic sectors of China. We use a novel fixed effect panel data quantile (FEQR) regression estimator and differentiate between energy-intensive and non-energy intensive sectors, as well as between more and less-developed provinces of China. We find evidence for an inverse U-shaped relation between patent generation and CO2 emissions for both, more and less energy-intensive sectors, suggesting that at low levels of innovation new technologies tend to be "dirty", but at high levels of innovation new technologies tend to be "green". The same relationship holds for less-developed provinces as well. For more-developed provinces, we find the opposite relation, which we explain with a "rebound effect".

Methodological framework for identifying sustainability intervention priority areas on coastal landscapes and its application in China

In regional sustainability evaluation and policy analysis, the paradigm of safe operating spaces (SOS) has been widely applied. Yet, SOS is not readily useful for informing policy interventions toward sustainability transition. This study reports on a methodological framework that operationalizes SOS at the regional scale for designing spatially targeted sustainability interventions. In particular, this framework accounts for teleology by integrating policy orientations of the place-variant "major function" of development, and provides early-warnings by integrating long-term social-environmental trends. The framework we proposed has been applied by the Chinese government in a coastal province (Liaoning) for a landscape sustainability project, which is introduced here step-by-step. The four main steps include: (1) Quantifying SOS status across multiple "what to sustain" dimensions, e.g., land scarcity, water scarcity, pollutant discharge, and ecosystem health for the inland, and coastal exploitation intensity, marine environmental quality, and marine ecosystem biodiversity for the sea. (2) Quantifying SOS status in terms of the place-variant "what to develop" dimensions, e.g., urbanization-oriented, agriculture-stock-oriented, versus conservation-oriented development. (3) Integrating the two as a composite indicator of three ordinal levels to classify the current SOS status. (4) Developing a multi-level sustainability early-warning system by cross-analysis of the SOS status and social-environmental interaction trends (e.g., changes in, e.g., resource utilization efficiency, pollutant discharge, and eco-environmental quality). The potential use of the framework is demonstrated through the case of Liaoning Province, China, which helps policy-makers to identify priority areas for sustainability interventions. Methodological robustness and future directions of applying this multi-level sustainability early-warning system are further discussed.

Decoupling analysis of the industrial growth and environmental pollution in the Circum-Bohai-Sea region in China

Based on a comprehensive consideration of waste water (WW) and waste gas (WG), the Tapio decoupling model is constructed to explore the decoupling relationship between industrial growth and industrial pollution in the Circum-Bohai-Sea region (CBSR) of China from 2003 to 2016. By dividing 37 sample cities into three sub-regions, we conduct a comparative analysis to describe the spatial-temporal evolution of the decoupling states of industrial growth and environmental pollution. The results show the following: (1) Overall, the industrial WW discharge in 37 key cities has been decoupled from industrial growth, and the industrial development mode is relatively ideal. (2) The decoupling between industrial growth and industrial WW and WG emissions is more ideal in Beijing-Tianjin-Hebei (BTH) than in Midsouthern Liaoning (MSL). (3) There are two nodes for the decoupling between industrial growth and WW and WG in Shandong Peninsula (SDP), and the decoupling state between industrial growth and WG is better than the decoupling state between industrial growth and WW from 2003 to 2016. (4) From 2003 to 2016, the decoupling state between industrial growth and WW and WG in MSL is not ideal. The conclusions show that the decoupling relationship between industrial growth and environmental pollution in the CBSR is still quite variable and unstable; thus, differential treatment measures should be taken. To enhance the effectiveness of these measures, we will further study the main factors affecting the decoupling relationship, and conduct a comparative study in a larger scale.

Driving forces of China's multisector CO2 emissions: a Log-Mean Divisia Index decomposition

To figure out which factor contributes more on carbon emissions caused by energy consumption, this research took multisector analysis based on the Log-Mean Divisia Index Method (LMDI) and decoupling theory to assess the driving factors of carbon dioxide (CO₂) emissions in China's six sectors from 2003 to 2016. Our empirical results reveal that China's economy can be divided as three decoupling stages and exhibited a distinct tendency toward strong decoupling with a turning point in 2008. Thus, we discuss the impact of 2008 economic crisis on carbon emissions based on decomposition results. The empirical results of our study show the following five conclusions. (1) Most sectors in China are in weak decoupling state due to the inhibition of energy intensity on carbon emissions. (2) Different factors contribute differently to reducing emissions in different sectors, economic output has the most prominent effect, followed by energy intensity and population scale. (3) China's current carbon emission reduction measures benefit more on energy efficiency. (4) The economic crisis has greatly reduced energy efficiency and has no significant impact on other factors. (5) If all industries adjust their energy mix, carbon emissions in China can be reduced by almost 17% every year.

A regional-scale decomposition of energy-related carbon emission and its decoupling from economic growth in China

China, known as the largest carbon emitter and the second largest economy worldwide, has continued to put effort into the understandings of the main drivers of carbon emission and their decoupling statuses from its economic growth. Considering the significant differences of natural and social environments in different regions of China, this paper presents a regional-scale decomposition of energy-related carbon emission and its decoupling from economic growth by using the Logarithmic Mean Divisia Index (LMDI) and the Tapio decoupling method. The decoupling results indicate that carbon emissions in all regions show a stable decoupling trend from their economic development, which means that China is now on the right road for achieving a low-carbon economy. However, the decoupling status by the end of 2016 also indicates that most of the regions are still in the states of expansive coupling or weak decoupling, especially in Northwest (NW), which implies that the speed of decarbonization process is still not high enough. The decomposition results show that in all regions except NW, GDP per capita is the most influential factor leading to increasing carbon emissions, while energy intensity is the largest factor in reducing carbon emissions. In NW, both GDP per capita and energy intensity drive the increase in carbon emissions. The results in this paper could benefit China's regional policy-making and national strategies.

Decoupling Elasticity and Driving Factors of Energy Consumption and Economic Development in the Qinghai-Tibet Plateau

Decoupling of energy consumption and economic development is a key factor in achieving sustainable regional development. The decoupling relationship between energy consumption and economic development in the Qinghai-Tibet Plateau region is still unclear. This paper uses the logarithmic mean Divisia index (LMDI) decomposition method and Tapio elastic index model to analyze the decoupling degree and driving factors of energy consumption and economic development, and evaluates the decoupling effort level in Qinghai-Tibet Plateau from 2006 to 2016. The results indicate that the Qinghai-Tibet Plateau region showed a weak decoupling as a whole, and that only Tibet experienced expanding negative decoupling in 2006–2007 and an expansion link in 2007–2008. Economic scale is a primary factor that hinders the decoupling of energy consumption, followed by investment intensity and industrial energy structure. The cumulative promotion effect of research and development (R&D) efficiency and intensity and the inhibition effect of investment intensity cancel each other out. With the exception of Tibet and Xinjiang, all provinces in the Qinghai-Tibet plateau have made decoupling efforts. Decoupling efforts made by R&D efficiency contributed the most, followed by energy intensity and R&D intensity. This paper provides policy recommendations for the decoupling of energy consumption experience for underdeveloped regions.

Peaking Industrial Energy-Related CO2 Emissions in Typical Transformation Region: Paths and Mechanism

Reducing CO2 emissions of industrial energy consumption plays a significant role in achieving the goal of CO2 emissions peak and decreasing total CO2 emissions in northeast China. This study proposed an extended STIRPAT model to predict CO2 emissions peak of industrial energy consumption in Jilin Province under the four scenarios (baseline scenario (BAU), energy-saving scenario (ESS), energy-saving and low-carbon scenario (ELS), and low-carbon scenario (LCS)). We analyze the influences of various factors on the peak time and values of CO2 emissions and explore the reduction path and mechanism to achieve CO2 emissions peak in industrial energy consumption. The results show that the peak time of the four scenarios is respectively 2026, 2030, 2035 and 2043, and the peak values are separately 147.87 million tons, 16.94 million tons, 190.89 million tons and 22.973 million tons. Due to conforming to the general disciplines of industrial development, the result in ELS is selected as the optimal scenario. The impact degrees of various factors on the peak value are listed as industrial CO2 emissions efficiency of energy consumption > industrialized rate > GDP > urbanization rate > industrial energy intensity > the share of renewable energy consumption. But not all factors affect the peak time. Only two factors including industrial clean-coal and low-carbon technology and industrialized rate do effect on the peak time. Clean coal technology, low carbon technology and industrial restructuring have become inevitable choices to peak ahead of time. However, developing clean coal and low-carbon technologies, adjusting the industrial structure, promoting the upgrading of the industrial structure and reducing the growth rate of industrialization can effectively reduce the peak value. Then, the pathway and mechanism to reducing industrial carbon emissions were proposed under different scenarios. The approach and the pathway and mechanism are expected to offer better decision support to targeted carbon emission peak in northeast of China.

Decomposition and decoupling analysis of energy-related carbon emissions in Turkey

This study focuses on CO₂ emission trends and its decompositions as well as decoupling performance between CO₂ emissions and economic growth of Turkish case for the period of 1990-2016. The drivers of CO₂ emission changes are calculated by using an extended Kaya identity and the well-established logarithmic mean Divisia index (LMDI) method. Decomposition results indicate that economic growth and population effects are the main driving forces in increases in carbon emissions in Turkey throughout the whole period, while other technology-based driving factors' impacts have been rather minimal in reducing the emissions. Decoupling analysis results demonstrate that there is either no decoupling or weak decoupling in most of the years. Moreover, total decoupling effort index suggests that Turkey's performance has been worsened in recent years as we found no decoupling between CO₂ emissions and economic growth over the period of 2013-2016. The overall findings suggest that Turkish economic growth is unsustainable both environmentally and economically. Based on these findings, some policy implications and recommendations are discussed for the possible emission reductions.