Logarithmic Mean Divisia Index Method Research Articles

Decomposition and Scenario Analysis of Factors Influencing Carbon Emissions: A Case Study of Jiangsu Province, China

It is crucial for China to take the characteristics and development stage of every province in the region into account in order to achieve the “dual carbon” development goal. Using data collected from 2000 to 2019, this study identifies the factors that influence carbon emissions using the logarithmic mean Divisia index (LMDI) method and establishes a revised stochastic impacts by regression on population, affluence, and technology (STIRPAT) model to investigate the effects of four key factors on carbon emissions in Jiangsu province: population size, economic output, energy intensity, and energy structure. The following conclusions were drawn: (1) energy intensity contributes to a slowed rate of carbon emission production in Jiangsu, whereas population size, economic output, and energy structure contribute to a pulling effect; (2) under different scenarios, Jiangsu’s carbon dioxide emissions peak at different times and reach different values; and (3) two low-carbon scenarios are more in line with the current development situation and future policy orientation of Jiangsu Province and are therefore better choices. Our policy recommendations are as follows: (1) the development of economic and social activities should be coordinated and greenhouse gas emissions should be reduced; (2) the province’s energy structure should be transformed and upgraded by taking advantage of the “dual carbon” development model; and (3) regionally-differentiated carbon emission reduction policies should be developed.

Trends, Drivers, and Mitigation of CO2 Emissions in the Guangdong–Hong Kong–Macao Greater Bay Area

The Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is a national initiative aimed at building a world-class city cluster in China and whose trends, socioeconomic drivers of CO 2 emissions, and mitigation pathways are of great significance to the high-quality regional economic development. This study compiled the CO 2 emission inventories of the GBA from 2000 to 2019 and explored the key drivers of CO 2 emissions using the logarithmic mean Divisia index method. The results showed that CO 2 emissions in GBA slowed significantly after 2017 and have already been decoupled from gross domestic product (GDP) growth. Economic growth and energy intensity are the major factors driving and inhibiting the increase in GBA’s CO 2 emissions, respectively. The energy production and heavy manufacturing sectors have reduced their roles in driving the growth of GBA’s CO 2 emissions, with the service sector becoming the main driver. GBA achieved remarkable results in low-carbon development through industrial restructuring and upgrading. Industrial upgrades in Shenzhen and Hong Kong and technological advances in Shenzhen, Guangzhou, and Foshan have significantly curbed the growth in the GBA’s CO 2 emissions. The heterogeneity of cities in the GBA greatly increases the complexity of formalizing the allocation of emission reduction tasks and developing a roadmap for regional carbon neutrality. Graded emission reduction strategies and carbon peaking and neutrality policy recommendations for GBA cities are proposed. This study provides a scientific basis for the development of an action program for carbon peaking and neutrality in GBA cities and low-carbon development plans for other cities and regions.

Decomposition analysis of CO2 emissions of electricity and carbon-reduction policy implication: a study of a province in China based on the logarithmic mean Divisia index method

Abstract As the proportion of electricity in final energy consumption gradually increases, CO2 emissions reduction actions in the power system will become the key to achieving China’s carbon-peak and carbon-neutrality goals. It is essential to analyse and quantify the driving forces of CO2 emissions from electricity generation in the fossil-rich area in China. This paper aims to identify the characteristics of CO2 emissions generated by electricity and provide a basis for formulating CO2-reduction policies in power systems. First, we analyse the current state of CO2 emissions from electricity generation in Anhui Province that was dominated by fossil energy during the period 2010–19. Then, we apply the logarithmic mean Divisia index method to find the nature of the factors influencing the changes in CO2 emissions. Finally, we analyse the CO2-reduction measures of each side of the source–network–load–storage of the power system in Anhui through a power-system carbon-reduction path analysis model proposed in this study and provide policy suggestions. The results showed the following. (i) CO2 emissions in Anhui Province continued to increase from 2010 to 2019 and the trend in the growth rate of CO2 emissions presented approximately a u-shaped curve. (ii) Economic activity has always been the dominant factor driving the growth of electricity CO2 emissions. The increase in the proportion of renewable energy in power generation, the improvement in thermal power-conversion efficiency and the decrease in the intensity of power consumption are the three major driving factors for the reduction in CO2 emissions from power generation in Anhui. (iii) The CO2-reduction measures of the power system are provided in each link of the source–network–load–storage, such as developing the photovoltaic industry and building energy storage, upgrading and transforming coal-fired power stations, reducing the loss rate of transmission lines on the grid side and improving the efficiency of the utilization of electricity on the user side.

Decoupling analysis of water consumption and economic development of arid and semiarid regions in Northwest China

AbstractThe contradiction between China's economic development, its need for resources and the protection of the environment is ctitical. Scarce water resources have resulted in a considerable bottleneck restricting the economic development of water‐deficient areas. An objective evaluation of the decoupling state of water consumption and economic development has become an important indicator of regional economic sustainable development. Based on panel data from 2000 to 2017 for six provinces in the arid and semiarid regions of Northwest China, the Logarithmic Mean Divisia Index method is employed to decompose the factors of the decoupling index and establish a decoupling relationship model between water consumption and economic development. The reasons that affect the decoupling state of water resource utilization and economic development are herein discussed, and the stability of the decoupling trend is analysed. Based on the overall regional trend, the decoupling state of the arid and semiarid regions in Northwest China improved from weak to strong, but the high decoupling stability index varied among the provinces. The intensity and structure were promotional factors for decoupling water consumption and economic development, and the contribution rate of the intensity factor was higher than that of the structure factor. Income and population were inhibiting factors for decoupling water consumption and economic development, and the contribution rate of the income factor was higher than that of the population factor. Based on these results, corresponding policy recommendations are provided.

Decomposing the decoupling relationship between energy consumption and economic growth in China's agricultural sector

Sustainable food supply is affected by high energy consumption and negative environmental effects. Regarding the national strategy of “carbon peaking and carbon neutrality targets”, the decoupling between energy consumption and economic growth in China's agriculture has received significant attention. Therefore, this study first presents a descriptive analysis of the energy consumption in China's agricultural sector from 2000 to 2019, before analyzing the decoupling state between energy consumption and agricultural economic growth at the national and provincial levels using the Tapio decoupling index. Finally, the logarithmic mean divisia index method is used to decompose the decoupling driving factors. The study draws the following conclusions: (1) At the national level, the decoupling of agricultural energy consumption from economic growth fluctuates among expansive negative decoupling, expansive coupling, and weak decoupling, before stabilizing in the last state. (2) The decoupling process also differs by geographic region. Strong negative decoupling is found in North and East China, and strong decoupling lasts longer in Southwest and Northwest China. (3) The factors driving decoupling are similar at both the levels. The economic activity effect promotes the decoupling of energy consumption. The industrial structure and energy intensity effects are the two main suppressive factors, whereas the population and energy structure effects have relatively weaker impacts. Therefore, based on the empirical results, this study provides evidence for regional governments to formulate policies on the relationship between the agricultural economy and energy management from the perspective of effect driven policies.

Impacts of meteorological factors and crop area changes on the variations in winter wheat water requirements in the lower reaches of the Yellow River Basin

Meteorological factors and human activities (such as changes in crop area) have exerted great impacts on the agricultural water resources. A thorough understanding of the changes in crop water requirements (CWR) and irrigation water requirements (IR) is crucial for effective agricultural water management. In the present study, the unit area and total volume of winter wheat CWR and IR were estimated based on meteorological and crop area data, and spatiotemporal variations were analyzed from 2001 to 2018 in the lower reaches of the Yellow River Basin. The impacts of meteorological factors and crop area changes on the CWR and IR variations were explored using sensitivity analysis and the logarithmic mean Divisia index method along with the identification of the main driving factors. The results revealed that both the unit area CWR and IR exhibited a decreasing trend with a multi-year average value of 540 mm and 488 mm, respectively, and increased gradually from south to northwest in the spatial distribution. The significant decrease in wind speed exerted the greatest impact on the unit area CWR and IR changes with the highest sensitivity among considered meteorological factors. The total volume of CWR and IR increased by 56.5 × 108 m3 and 32.3 × 108 m3 from 2001 to 2018, respectively. The crop area change of winter wheat was a major contributor to the variations in total volume of CWR and IR rather than meteorological factors during the study period, and evident spatial and temporal differences were observed. The results of this study provide valuable insights for developing adaptive strategies considering the impacts of meteorological factors and planted crop area changes and can therefore contribute to the sustainable utilization and management of agricultural water resources.

Analysing the Effect of Energy Intensity on Carbon Emission Reduction in Beijing.

Beijing has experienced rapid economic development since the reforms and opening up. However, the traditional development model based on excessive energy consumption has posed great challenges to the ecological environment. To curb environmental degradation and achieve sustainable social development, Beijing has proposed to achieve carbon neutrality by 2050. As an important indicator of energy consumption, it is necessary to clarify how energy intensity (EI) affects carbon emissions (CE) to achieve carbon neutrality in Beijing by 2050. This study first decomposes the drivers of CE in Beijing from 2010 to 2020 using the logarithmic mean Divisia index (LMDI) method and comparatively analyses the impact of EI on CE. Then, the spatial Dubin model (SDM) is used to analyse the spatial spillover effect of EI on CE at the regional level. Finally, the macro moderating role of economic development in the effect of EI on CE is analysed. The results show that the effect of EI has been the main driver of CE reduction in Beijing. Among the industrial sectors, manufacturing and transportation have had the greatest success in reducing CE through EI reduction. At the regional level, there is a spatial spillover effect of EI on CE, and the effect of carbon reduction through the spillover effect of EI is greater than the direct effect of EI. Economic factors have an enhanced moderating effect on the process of EI affecting CE, and this moderating effect has threshold properties.

Simulation of carbon peaking process of high energy consuming manufacturing industry in Shaanxi Province: A hybrid model based on LMDI and TentSSA-ENN.

To achieve the goals of carbon peaking and carbon neutrality in Shaanxi, the high energy consuming manufacturing industry (HMI), as an important contributor, is a key link and important channel for energy conservation. In this paper, the logarithmic mean Divisia index (LMDI) method is applied to determine the driving factors of carbon emissions from the aspects of economy, energy and society, and the contribution of these factors was analyzed. Meanwhile, the improved sparrow search algorithm is used to optimize Elman neural network (ENN) to construct a new hybrid prediction model. Finally, three different development scenarios are designed using scenario analysis method to explore the potential of HMI in Shaanxi Province to achieve carbon peak in the future. The results show that: (1) The biggest promoting factor is industrial structure, and the biggest inhibiting factor is energy intensity among the drivers of carbon emissions, which are analyzed effectively in HMI using the LMDI method. (2) Compared with other neural network models, the proposed hybrid prediction model has higher accuracy and better stability in predicting industrial carbon emissions, it is more suitable for simulating the carbon peaking process of HMI. (3) Only in the coordinated development scenario, the HMI in Shaanxi is likely to achieve the carbon peak in 2030, and the carbon emission curve of the other two scenarios has not reached the peak. Then, according to the results of scenario analysis, specific and evaluable suggestions on carbon emission reduction for HMI in Shaanxi are put forward, such as optimizing energy and industrial structure and making full use of innovative resources of Shaanxi characteristic units.

The Increasing Role of Synergistic Effects in Carbon Mitigation and Air Quality Improvement, and Its Associated Health Benefits in China

A synergistic pathway is regarded as a critical measure for tackling the intertwined challenges of climate change and air pollution in China. However, there is as yet no indicator that can comprehensively reflect such synergistic effects; hence, existing studies lack a consistent framework for comparison. Here, we introduce a new synergistic indicator defined as the pollutant generation per gross domestic product (GDP) and adopt an integrated analysis framework by linking the logarithmic mean Divisia index (LMDI) method, response surface model (RSM), and global exposure mortality model (GEMM) to evaluate the synergistic effects of carbon mitigation on both air pollutant reduction and public health in China. The results show that synergistic effects played an increasingly important role in the emissions mitigation of SO 2 , NO x , and primary particulate matter with an aerodynamic diameter no greater than 2.5 μm (PM 2.5 ), and the synergistic mitigation of pollutants respectively increase from 3.1, 1.4, and 0.3 Mt during the 11th Five-Year Plan (FYP) (2006–2010) to 5.6, 3.7, and 1.9 Mt during the 12th FYP (2011–2015). Against the non-control scenario, synergistic effects alone contributed to a 15% reduction in annual mean PM 2.5 concentration, resulting in the prevention of 0.29 million (95% CI: 0.28–0.30) PM 2.5 -attributable excess deaths in 2015. Synergistic benefits to air quality improvement and public health were remarkable in the developed and population-dense eastern provinces and municipalities. With the processes of urbanization and carbon neutrality in the future, synergistic effects are expected to continue to increase. Realizing climate targets in advance in developed regions would concurrently bring strong synergistic effects to air quality and public health.

Prediction and scenario simulation of the carbon emissions of public buildings in the operation stage based on an energy audit in Xi'an, China

The carbon emissions (CEs) of public buildings (PBs) in the operation stage significantly impact building life cycle assessments (LCA). Energy conservation and carbon reduction during this stage is necessary for China to achieve peak CEs and neutralization. Energy consumption and CEs are analyzed using energy audit data for PBs in Xi'an. CEs of PBs in the operation stage are calculated by our prediction method with an average deviation of only 4.41%. Using the logarithmic mean Divisia index (LMDI) method to analyze factors influencing CEs, we find that economic development levels impact CEs the most in the operation stage; the impact coefficient reaches 3.81. This study discusses the contribution of various driving factors of CEs via the stochastic impacts by regression on population, affluence, and technology (STIRPAT) method and simulates seven scenarios to predict change trends of the CEs of PBs in the operation stage. The rough development scenario fails to achieve the peak, the comprehensive optimization development scenario achieves the peak in 2040, and the remaining scenarios achieve the peak in 2050. The results provide practical and theoretical support for CE research and a scientific basis for predicting the CEs of PBs in the operation stage throughout China and worldwide.

Innovation and Climate Change Mitigation Technology in the Asian and African Mining Sector: Empirical Analysis Using the LMDI Method

Technology plays an essential role as climate change becomes a growing concern worldwide. This article aims to examine the influence that innovation exerts on climate change mitigation technology (CCMT) in the African and Asian mining sectors. Data were collected from the World Intellectual Property Organization mining database. We conducted a decomposition analysis of patent families between 2011 and 2020 based on the Logarithmic Mean Divisia Index (LMDI) method. Findings revealed that African countries do not devote their innovation efforts to adaptive technologies, resulting in a mismatch between mining and access to technologies as the scope of R&D narrows. In Asia, the drive for innovation and technological efficiency is a tool to prevent economic damage and legitimize technological benefits as solutions for climate change mitigation technology. This outcome calls on political, national, and international governments to bridge the innovation gap to trigger a real shift from innovation to these technologies.

Factor decomposition for ecological pressure of the whole industrial energy carbon footprint: a case study of China.

The purpose of this paper is to study the influencing factors of the ecological pressure of the energy carbon footprint (EPECF) of China's whole industry from 2000 to 2018. First, the EPECF of 48 sub industries is calculated, then divides 48 sub-industries into high-, medium-, and low-pressure industries, and uses the logarithmic mean Divisia index (LMDI) method to analyze and summarize the main driving forces of China's industrial EPECF changes. Finally, policy suggestions for the future industrial decompression are put forward. The main results are as follows: (1) Economic development is the most important factor to promote the growth of EPECF of the three major industries. (2) At present, the population pressure factors of forest and grassland have little effect, and the effect of returning farmland to forest and grassland has not been truly played. (3) The adjustment of industrial structure has gradually become a key factor in reducing EPECF of the three industries. (4) The gradual stability of energy intensity has a certain inhibitory effect on the increase of EPECF in high-pressure industry. (5) The adjustment of energy structure in low-pressure industry has gradually worked. Therefore, the government should establish an economic sustainable development system, vigorously develop clean energy, and realize the green transformation of various industries. This provides an empirical example for other countries in the world to reduce the EPECF.

Future outlook and influencing factors analysis of natural gas consumption in Bangladesh: An economic and policy perspectives

The development of mechanization and technology has triggered the rising energy consumption in various sectors. The study objective is to investigate the relationship between gas consumption share, energy intensity, economic structure share, per capita gross domestic value, and population at the sectorial level from 1994 to 2020. This study analyzes the factors in three ways: first, the study applies decomposition analysis between key factors. Second, an appropriate decoupling method is applied to investigate the impacting factors to check the driving factors affecting the decoupling using the logarithmic mean Divisia index method. Third, based on aggregated and sectorial variations, the ratio decomposition of each sector is estimated. The results show that the economic activity effect is the main driving factor in raising gas consumption, which increased by 15.11 crore Tk./million during 1994–2020. The energy intensity trend declined by 14.87 Mtoe/crore Tk., which shows an imperative role in natural gas consumption efficiency for production and in economy. The economic structure share provided a record decline by 52.79 crores Tk. during the COVID-19 situation while a significant increase in gas consumption was found until 2013, which could be significant under decomposition changes. Three decoupling states such as expansive negative decoupling, expansive coupling and weak decoupling were found in which the decoupling relationship was gradually a dominated by a weak decoupling. The sectorial index provides a maximum of weak decoupling in which energy-saving technology gap change was found in power, captive power, industry, tea estate, compressed natural gas, and fertilizer sectors. Finally, the scenarios analyzed that natural gas consumption estimates maximum gross domestic product and conserves more energy in the future. Briefly, the decoupling process and its leading factors were obvious among nine major sectors.

A novel spatio-temporally stratified heterogeneity model for identifying factors influencing carbon emissions

Understanding factors influencing carbon emissions is important for achieving carbon abatement goals. Traditionally, decomposition approaches, e.g. Logarithmic Mean Divisia Index (LMDI) method, are used to uncover factors influencing the change of carbon emissions. This study aims to develop a novel spatio-temporally stratified heterogeneity (STSH) model to better identify, analyze and understand influencing factors of carbon emissions. Compared with LMDI, this model can analyze more independent intensity and quantity factors and reveal the complex interactions between factors. Using China’s construction industry as a case study, the model can successfully identify top influencing factors in the same order of importance similar to LMDI, although a significantly larger number of factors are considered. In the whole construction stage, cement usage, steel usage, completed floor area and construction output value have the highest contributions of 0.599, 0.528, 0.448, 0.446 respectively. In construction and construction-related transportation activities, the top influencing factors are fixed capital assets, construction output value, completed floor area, and electricity usage, with contributions of 0.632, 0.613, 0.599, and 0.597, respectively. The model can also reveal the complex interactions between factors, including bi-variate enhanced interaction and nonlinearly enhanced interaction. The results demonstrate that the model is more useful to evaluate the individual and aggregate impact of a large number of independent factors on carbon emissions when compared with previous models.

Environmental and Socioeconomic Determinants of Virtual Water Trade of Grain Products: An Empirical Analysis of South Korea Using Decomposition and Decoupling Model

The world’s sustainable growth is being severely hampered by the inefficient use of water resources. Despite the widely acknowledged importance of trade in global and regional water and food security, societal reliance on local production, as well as international trade, remains inadequately assessed. Therefore, using South Korea as a case study, this study fills in this research gap by applying the virtual water concept, the logarithmic mean divisia index (LMDI) method, and the Tapio decoupling model. The virtual water concept was used to estimate South Korea’s net virtual water trade for major grain crops from 1992 to 2017. Then, the LMDI method was utilized to assess the driving factors causing changes in net virtual water trade. Lastly, the Tapio decoupling model was used to investigate the decoupling relationships between economic growth and the driving factors of net virtual water trade. The results showed that South Korea remains a net importer of virtual water flows with respect to grain crops, with an average import of 16,559.24 million m3 over the study period. In addition, the change in net virtual water trade could be attributed to the water intensity effect, product structure effect, economic effect, and population effect. However, water intensity and economic effects were the major decisive factors for decreases and increases in net virtual water trade, respectively, while the population and product structure effects had minor positive influences on the net virtual water trade. Furthermore, water intensity and economic growth showed a strong decoupling in most periods, while the decoupling state between product structure and economic growth was observed as expansive negative decoupling. Likewise, population size and economic growth showed a weak decoupling in most periods. The results reveal South Korea’s status as it concerns the virtual water trade of grain crops, thus providing valuable insights into the sustainability of trade activities for the management of local water resources.

Changes in crop water consumption in Xinjiang of China from 1989 to 2018: A Logarithmic Mean Divisia Index decomposition analysis

Xinjiang, located in Northwestern China, is the important production base of various crops with high water consumption. The quantitative contribution of driving factors to crop water consumption has not been investigated in Xinjiang. In this study, the Logarithmic Mean Divisia Index method is used to quantitatively analyze the effect of five factors (population, planting structure, agricultural economics, water intensity, and industrial structure) to crop water consumption during 1989–2018. The results show that 1) crop water consumption has increased from 10.363 to 37.226 billion m3 with a rate of 0.932 billion m3/a in 1989–2018. Its increased trend can be divided into two stages: a slow increase at a rate of 0.425 million m3/a in 1989–2003 and a quick expansion at a rate of 1.310 million m3/a in 2004–2018. 2) The increase of population and agricultural economics both promote crop water consumption, whereas changes in planting structure and water intensity both inhibit crop water consumption. Their contributions are 0.213, 2.068, −0.007, and −0.134 billion m3, respectively. The increased agricultural economics and the decreased water intensity more significantly changed crop water consumption in 2004–2018 than in 1989–2003. 3) The total effects of five factors on crops varied at each stage. All crops (except wheat) have a promoting effect on an increase in crop water consumption with the largest value in cotton (0.378 million m3) in 1989–2003. The effect of the five factors on crops (except soybean and medicago) is positive (1.404 million m3), and the highest value is shown in cotton during 2004–2018. The results illustrate the contribution of the five factors of crop water consumption and provide references for local agricultural water saving in Xinjiang.

CO2 emissions in China's power industry by using the LMDI method.

WIth the introduction of "carbon peak and neutrality" targets, China's power industry is under enormous pressure to reduce carbon dioxide (CO2) emissions, as it produces more than 40% of emissions. In response, China's power industry is actively reducing the investment in thermal energy and gradually shifting toward non-fossil energy sources. However, the CO2 reduction effect of these measures is still unknown. This study aims to analyze CO2 emissions from China's power industry from 2009 to 2018 from an entire lifecycle perspective, considering that CO2 emissions also exist in non-fossil power generation. The logarithmic mean Divisia index (LMDI) method is employed to identify the factors influencing CO2 emissions. Then, the modified STochastic Impacts by Regression on Population, Affluence and Technology model is used for comparative validation. The results show that (1) CO2 emissions from China's power industry increased significantly, from 276.5 million tons of CO2 equivalent (Mtce) in 2009 to 436.44 Mtce in 2018; (2) the investment intensity, investment structure, and emission intensity dampen CO2 emissions, with cumulative contribution rates of - 28.88%, - 11.89%, and - 3.16%, respectively. The investment efficiency, economic development level, and population size contribute to CO2 emissions, with cumulative contribution rates of 29.76, 24.68, and 1.07%, respectively; and (3) Investment into the hydropower contributes the least to CO2 emissions, followed by wind, nuclear, photovoltaic, and thermal power. These research findings suggest that the power industry should improve its investment decision-making capabilities and pay particular attention to the hydropower-led non-fossil energy sector.

Reduce carbon emissions efficiently: The influencing factors and decoupling relationships of carbon emission from high-energy consumption and high-emission industries in China

High-energy consumption and high-emission industries contribute a lot to economic development, but their carbon emissions are also huge. In order to achieve the dual-carbon target as early as possible, it is necessary to reduce the carbon emissions of high-energy consumption and high-emission industries. This paper selected five representative factors (population, per capita gross domestic product (GDP), energy intensity, energy structure and carbon emission coefficient) and adopted the logarithmic mean divisia index (LMDI) method to decompose the driving factors of carbon emissions. Therefore, this paper uses Tapio decoupling model to analyze the decoupling relationship between the two factors with the greatest impact on carbon emissions and carbon emissions. The results show that: (i) There is a good decoupling between high-energy consumption and high-emission industries and per capita GDP, and the impact of per capita GDP on carbon emissions will gradually decrease in the future; (ii) The decoupling relationship between carbon emissions and energy intensity is poor. For some industries, the reduction of energy intensity can help reduce carbon emissions. Finally, this paper puts forward some suggestions to promote carbon emission reduction. This paper provides theoretical support for studying how to reduce carbon emissions and formulate relevant emission reduction policies in the high-energy consumption and high-emission industries.

Heterogeneity and Decomposition Analysis of Manufacturing Carbon Dioxide Emissions in China's Post-Industrial Innovative Megacity Shenzhen.

Effectively reducing manufacturing carbon dioxide (CO2) emissions is a vital strategy for China to curb its rapidly rising carbon footprint. Features of such a reduction in manufacturing include an increase in the share of high-tech manufacturing and optimization of the energy consumption structure. This study aims to analyze the case of Shenzhen, a unique post-industrial innovative manufacturing megacity, for its leading experience in China's manufacturing transition. Disaggregated manufacturing emissions data of Shenzhen, including 27 sub-sectors in four categories, were collected, and driving factors were identified by the logarithmic mean Divisia index (LMDI) method. The results suggest that: (1) CO2 emissions from Shenzhen's manufacturing show a phased difference between 2008-2012 and 2012-2020. CO2 emissions embodied in electricity consumption have increased by over 30% in the former period and have remained stable at a high level of over 90%. (2) Significant heterogeneity of CO2 emissions in various manufacturing sectors is revealed, with the largest emissions sources being factories that make communication equipment, computers, and other electronic equipment. (3) Lower carbon intensity is the primary factor in reducing CO2 emissions, while the economic activity effect of manufacturing possesses a stimulating impact. (4) The marginal impact of restructuring on CO2 emissions is insignificant since the manufacturing and energy structures of Shenzhen have been upgraded to a low carbon level. Therefore, strengthening the power saving management and improving the energy efficiency of the manufacturing, rather than optimizing the manufacturing and final energy structures, will be a necessary potential solution to the problem of how to reduce CO2 emissions in Shenzhen's manufacturing.

Carbon emissions trajectory and driving force from the construction industry with a city-scale: A case study of Hangzhou, China

China has promised the world that it will reach the carbon peak by 2030 and achieve carbon neutrality by 2060 (called as “dual carbon” target). Reducing Carbon Emissions from the Construction Industry (CECI) is critical to meeting the "dual carbon" target, as it shared 30% of the total carbon emissions. How to estimate carbon emissions at the city-scale is another issue. This study focuses on the materialization process of buildings and city-scale, establishing a framework to explore the carbon emissions trajectory. The driving force was clarified by the Logarithmic Mean Divisia Index method. The results show that in the case of Hangzhou in 2019, carbon emissions caused by fossil energy consumption in the construction site (called direct CECI) account for 3.3% of the total CECI. A large part of emission is from indirect CECI which includes the secondary energy (47.2%) and embodied carbon in material or production (38.9%), which are mainly emitted outside Hangzhou, because of building materials import. This paper also found that construction scale and demand structure were the main driving force of CECI growth, while the decrease in energy consumption will significantly restrain the increase of CECI. This study will contribute to the sustainable development of cities and society.