Logarithmic Mean Divisia Index Decomposition Research Articles

The Critical Role of Energy Intensity in Decarbonizing ASEAN: Integrating Growth and Emissions Reductions

This research analyzes drivers of CO2 emissions across ASEAN countries from 1971-2017 to inform effective policies for sustainable decarbonized development. The goal is to identify critical factors influencing emissions growth and reductions to guide strategic climate mitigation planning. Data includes 3128 emissions, GDP, population, and energy consumption observations from 9 ASEAN nations. Results using the Kaya Index and Logarithmic Mean Divisia Index decomposition reveal GDP growth and population as primary drivers of increasing emissions, while energy efficiency dampens growth. Indonesia saw the highest emissions growth, driven by population and economic expansion. Thailand's phenomenal GDP growth of 3003.7% led to its emission increases. Singapore and the Philippines achieved notable reductions. Findings suggest integrated policies like clean energy, efficiency gains, infrastructure planning, and low-carbon economic reform are needed. Transitioning towards affordable clean energy systems with proactive leadership can enable ASEAN to sustain growth while mitigating climate risks. Further research should identify optimal policy mixes to maximize socio-economic progress and welfare while meeting urgent emission targets tailored to each nation. This study provides vital insights into key factors influencing ASEAN emissions and highlights pathways towards sustainable decarbonized development. The analysis of historical trends aims to inform strategic policymaking for decarbonization.

Simulation and Forecasting Study on the Influential Factors of PM2.5 Related to Energy Consumption in the Beijing–Tianjin–Hebei Region

It is still necessary to regularly investigate the breakdown of socio-economic elements as a starting point for analyzing the effects of diverse human production activities on PM2.5 intensity from industrial and regional viewpoints. In this paper, the emission factor model was adopted to measure PM2.5 emissions in the Beijing–Tianjin–Hebei (BTH) region at the regional and industrial levels. The logarithmic mean Divisia index (LMDI) decomposition model was employed to analyze the factors affecting PM2.5 emissions related to energy consumption in the BTH region at the regional and sectoral levels. Building on this foundation, a system dynamics (SD) model was established to make a prediction regarding PM2.5 pollution in the BTH region in 2025. This study found that secondary industry was a major source of PM2.5 emissions in the BTH region. Coal remained the main form of energy consumption in the BTH region. Secondly, the effect size of the factors affecting PM2.5 intensity ranked in the order of energy intensity, energy structure, and industrial structure. Thirdly, in 2025, PM2.5 emissions in the BTH region will decline appreciably, but there is still a certain gap in terms of meeting the targets of “the 14th Five-Year Plan” between the three provinces and cities. These results indicate that the BTH region should achieve the effective management of PM2.5 pollution at the source through the following initiatives: it is necessary to carry out the continuous adjustment of energy structures to gradually increase the proportion of clean energy; we must steadily promote the decline in energy intensity reduction, and gradually strengthen scientific and technological innovation; and we must continue to promote the optimization of the industrial structure and increase the proportion of tertiary industry every year.

Analysis of decoupling between CO2 emissions and economic growth in China's provincial capital cities: A Tapio model approach

Large amounts of CO2 emissions often accompany economic growth. The decoupling analysis is a common approach to assess how economic growth can be achieved without significant negative environmental impacts and by promoting energy conservation and emission reduction. Cities are essential in mitigating climate change problems and promoting low-carbon living. This study analyzes the relationship between economic development and CO2 emissions in Chinese provincial capitals from 2011 to 2021. The Tapio decoupling model and Logarithmic Mean Divisia Index (LMDI) decomposition model are used to assess this decoupling relationship based on panel CO2 emissions and GDP data. The results show that: (1) CO2 emissions are still growing at a slow pace, but with big fluctuations during 2019–2021 caused by the COVID-19 pandemic; (2) Cities have significant differences, with some cities reaching a stable weak decoupling, and others with unstable decoupling. Also, economically developed cities have significant advantages; (3) Economic output is still the main factor inhibiting decoupling. The inhibiting effect of population growth is gradually stabilizing. Results demonstrate that energy technology and economic structure have a great role in promoting decoupling. In conclusion, this study emphasizes the importance of energy efficiency and sustainable development through advanced technologies, economic restructuring, and low-carbon strategies for transition toward climate-resilient urban development.

Spatio-temporal patterns and impact mechanisms of CO2 emissions from China's construction industry under urbanization

The construction industry fuels China's rapid urbanization, which also escalates carbon emissions. This study aims to investigate the spatio-temporal patterns and impact mechanisms of construction carbon emissions (CCE) under urbanization to inform low-carbon policies. We investigate the spatial heterogeneity and temporal trends of China's CCE employing the carbon emission coefficient method and the backpropagation neural network, identify various development patterns of CCE under urbanization using the K-means clustering method, and further comprehensively explore the key influencing factors through the logarithmic mean Divisia index decomposition method. Our findings indicate that: (1) CCE presents a notable spatio-temporal polarization and aggregation effect during 2009–2018 in China. Compared with rough development scenario, the green development scenario is predicted to effectively reduce CCE by 5.17 % in 2030. (2) The relationship between urbanization and CCE is revealed to be an “inverted U-shape” and four different development patterns are identified. (3) Overall, the output intensity and structural effect of fixed asset inputs are critical factors contributing to CCE. Importantly, the impact mechanisms of CCE vary under different development patterns. This study provides essential evidence-based information and scientific support for CCE reduction in China, which highlights targeted and tailored strategies for reducing CCE considering different patterns under urbanization.

Decoupling Economic Growth from Carbon Emissions in the Yangtze River Economic Belt of China: From the Coordinated Regional Development Perspective

Decoupling economic growth from carbon emissions is crucial for combating the climate crisis and promoting green development. However, a uniform approach to climate mitigation exacerbates regional disharmony. As a microcosm of China’s regional heterogeneity, the Yangtze River Economic Belt (YREB) is helpful in exploring regional collaborative climate governance. This paper uses the Thiel index, the Tapio decoupling model, and the Logarithmic Mean Divisia Index (LMDI) decomposition approach to explore the decoupling of economic growth from carbon emissions in YREB from 2005 to 2019. Results indicate that the carbon intensity difference is mainly from the difference within middle-rising provinces (MRP) and western less-developed provinces (WLP). YREB exhibits strong decoupling overall, but it is not sustained. The economic growth effect significantly promotes carbon emissions, which is more prominent in MRP. The energy intensity effect plays a vital role in restraining carbon emissions. The emission factor effect signals an improved energy structure in WLP. Regional coordination is needed to achieve green development; thus, provinces should set differentiated carbon emission reduction targets, and more potent tools are recommended in major carbon emitters.

Carbon emissions in the logistics industry: driving factors and decoupling effects.

The explosive growth of the logistics industry has led to an increase in energy consumption and carbon emissions. To reduce emissions and increase the efficiency of the logistics industry, we studied the driving factors and decoupling effects of carbon emissions of logistics industry (LICE). First, an energy coefficient method is used to calculate the LICE. Second, the Logarithmic Mean Divisia Index (LMDI) decomposition method is used to decompose the driving factors of LICE into five types. Third, the decoupling model is used to explore the decoupling relationship between economic growth and LICE. Considering Anhui Province as an example, this study describes the method's implementation process based on a comparison of the four Yangtze River Delta provinces and cities. The results indicate that the growth rate of LICE in Anhui Province has decreased during the research period, from 9.7% in 2013 to 2.1% in 2021; however, the Tapio decoupling elasticity has been approximately 0.4 for the last 2 years, remaining in a weak decoupling stage from economic development. The LMDI decomposition results indicate that the average contribution of economic level to carbon emissions is 1.763. This study proposes some solutions and recommendations for the logistics industry's low-carbon development to offer methodological and theoretical support for LICE research.

Assessing Carbon Emissions from Animal Husbandry in China: Trends, Regional Variations and Mitigation Strategies

The intensification of land use and the accelerated integration of three industries (agricultural production, agricultural product processing industry, and agricultural product market service industry) in China have resulted in the continuous expansion of animal husbandry and its industrial chain. This phenomenon has led to a rise in greenhouse gas (GHG) emissions from livestock farming, intensifying climate change and placing strain on worldwide environmental conservation efforts. Life cycle assessment (LCA) was utilized to evaluate carbon emissions from China’s animal husbandry sector from 2012 to 2021. Additionally, the logarithmic mean divisia index (LMDI) decomposition method was employed to examine and elucidate the influential impacts of five factors on carbon emissions. These factors included the efficiency of animal husbandry production, the structure of the agricultural industry, per capita agricultural production income, urbanization, and the total population. The results reveal the following: (1) From 2012 to 2021, China’s animal husbandry sector witnessed a marginal increase in cumulative carbon emissions from 287.74 million tons to 294.73 million tons, with an annual growth rate of 0.42%. (2) Emission contributions were categorized as follows: the production stage (149.61 million tons), the transportation stage (145.07 million tons), and the processing stage (0.05 million tons). (3) The primary factor contributing to the rise in carbon emissions from animal husbandry from 2012 to 2021 was the per capita agricultural production income factor (A3), alongside a notable impact from the total population factor (A5).

Differential Quantitative Analysis of Carbon Emission Efficiency of Gansu Manufacturing Industry in 2030

Decomposition analysis and forecasting of carbon emissions in manufacturing are crucial for setting sustainable carbon-reduction targets. Given the varied carbon-emission efficiencies across sectors, this study applied the Logarithmic Mean Divisia Index (LMDI) decomposition method to analyze the drivers of carbon emissions in Gansu’s manufacturing sector, encompassing high, medium, and low-efficiency industries, and it identified vital factors affecting carbon emissions. A localized Long-range Energy Alternatives Planning System (LEAP) model for Gansu was also developed. This model includes six developmental scenarios to project future carbon emissions. The study results are as follows: (1) LMDI decomposition indicates that increased carbon emissions in the manufacturing industry primarily result from economic growth in less efficient sectors and the dominance of moderately efficient ones. (2) Under Optimization Scenario 6, a 50.82 × 104 ton reduction in carbon emissions is projected for Gansu’s manufacturing sector by 2030 compared to 2020, marking the carbon peak. These outcomes provide valuable insights for policy reforms in Gansu’s manufacturing industry, aiming for carbon peaking by 2030.

Research on the path of building carbon peak in China based on LMDI decomposition and GA-BP model.

The building sector contributes significantly to carbon emissions, impeding China's progress toward its 2030 carbon emissions peak target due to the limited utilization of renewable energy sources. This study aims to forecast the peak and timing of carbon emissions in China's construction industry to chart a low-carbon roadmap for the sector's future. Initially, an extended logarithmic mean divisia index (LMDI) decomposition model, based on the Kaya identity, is proposed to gauge the contribution levels of driving factors affecting building carbon intensity. Subsequently, a hybrid prediction model (IGA-BP) is constructed, employing an optimized two-hidden-layer neural network via a genetic algorithm, to forecast building carbon emissions and intensity. Additionally, four scenarios are outlined, each defining pathways to simulate emissions peak, carbon peak timing, and intensity within the Chinese building sector from 2020 to 2050. The research findings reveal: (1) The final emission factor of buildings primarily drives the surge in building carbon intensity, while the industrial structure stands as the most significant limiting factor. (2) Compared to alternative models, the proposed hybrid prediction model more effectively captures the evolution pattern of carbon emissions. (3) The prediction results indicate that China's building carbon intensity has reached its peak. Pathway 12 closely aligns with the sector's carbon emissions peak, projecting a peak value of 5.609 billion tons in 2029. To attain this pathway, China needs to develop more precise and feasible emission reduction strategies for its buildings. Overall, the research outcomes furnish robust references for decision-making in future efforts aimed at reducing building emissions.

Analysis of the Forces Driving Public Hospitals’ Operating Costs Using LMDI Decomposition: The Case of Japan

The sustainable management of public hospitals is usually threatened by long-term operating deficit, which was exacerbated during the COVID-19 pandemic. This study aimed to quantitatively decompose the historical changes in the annual operating costs of public hospitals in Japan to identify the main driving forces responsible for a worsening imbalance between operating costs and income over the past two decades. A dataset of the annual operating costs of public hospitals in Japan was compiled, in which influencing factors were redefined to make the data amenable to the application of a decomposition method referred to as the Logarithmic Mean Divisia Index (LMDI). Using the LMDI method, the contribution of each influencing factor to the changes in public hospital operating costs was quantitatively determined. The results indicate that, on average, there is an annual reduction in operating costs by JPY 9 million per hospital, arising out of the national reform of public hospitals, but the rapid increase in the prices and worsened structure of costs in recent years resulted in an annual increment of JPY 127 million per hospital to the increasing operating costs. The pandemic revealed damage to the financial balance of public hospitals, but epidemic prevention policies brought an offset to the increased operating cost. A more resilient domestic medical supply chain, the introduction of new technologies, and continuous endeavors in system reform and pricing policies are required to achieve financial sustainability in public hospitals in Japan.

Effects of trade liberalization on the global decoupling and decomposition of CO2 emissions from economic growth

Evidence of climate change is widespread and severe across all parts of the world. This poses a threat to humanity, and the entire environment. Appropriate policies are therefore required to help reduce greenhouse gas emission levels, limit the rate of global warming and its impact on climate change while pursuing national growth targets. This study employs the Tapio decoupling method to analyse the decoupling relationship (DR) between economic growth and carbon dioxide (CO2) emissions from 1998 to 2018. We also apply the Logarithmic Mean Divisia Index (LMDI) decomposition method on an extended Kaya identity to analyse CO2 emissions drivers in 145 countries. Last, the study examined the relative impacts of trade intensity and trade efficiency on the DR between economic growth and CO2 emissions. The results revealed that regions with relatively many developing and emerging countries (i.e., SSA, EAP, LAC, MENA, and SA) generally performed Weak Decoupling (WD), Expansive Negative Decoupling (END) and Expansive Coupling (EC), and the decoupling process was largely unstable. The ECA and NA regions on the other hand, which are typically composed of developed economies performed stable WD and Strong Decoupling (SD) statuses throughout the study period. The evidence further revealed that while trade intensity, activity, population, output per carbon emission and Carbon Intensity (CI) effects promote CO2 emissions, trade efficiency and energy intensity (EI) hinder emissions. We recommend that developing countries should enforce laws and cooperate with the developed economies to gain access to green technology to promote environmental sustainability.

Spatiotemporal characteristics and zonal analysis of carbon balance in county-level regions: case study of Jiangsu Province

In China, county-level regions play a pivotal role in achieving dual carbon goals. In this study, we analyzed the spatiotemporal characteristics of carbon balance coefficients for counties in Jiangsu Province from 2000 to 2021. The logarithmic mean Divisia index decomposition method explored the factors influencing county-level carbon balance. The findings indicated that CO2 emissions in Jiangsu Province increased from 192 million tons in 2001 to 637 million tons in 2011 and exhibited a small fluctuation from 2012 to 2021. A “higher in the south, lower in the north” distribution pattern emerged, with high CO2 emission areas concentrated in the economically developed south. Vegetative carbon sequestration increased from 142 million tons in 2000 to 171 million tons in 2021 with a “higher boundary and lower at the center” distribution pattern. High carbon sequestration areas were mainly in regions with abundant natural resources and vegetation. The number of counties experiencing carbon imbalance rose from 63 in 2000 to 95 in 2021, with a spatial distribution similar to high carbon emissions. Economic development and urbanization accelerated carbon imbalance, whereas technological advancement and reduced regional carbon fixation pressure effectively alleviated it. Pursuing of dual carbon goals can be supported by reducing emissions and increasing carbon sinks.

Decomposition of agriculture-related non-CO2 greenhouse gas emissions in Chengdu: 1995–2020

Methane (CH4) and nitrous oxide (N2O) emissions originating from agricultural systems exert a significant influence on climate change. The exploration of potential driving factors and emission trends based on a city is important for policy-makers and land managers, however has been rarely known. We aim to investigate the potential driving factors behind five non-CO2 sources within Chengdu's agricultural sector spanning 1995 to 2020, including rice cultivation (CH4), agricultural land (N2O), manure management (CH4 and N2O), and enteric fermentation (CH4), and project non-carbon dioxide (CO2) greenhouse gas (GHG) emissions in Chengdu's agricultural sector for the period from 2021 to 2035. The logarithmic mean Divisia index (LMDI) decomposition model was adopted to figure out the influencing factors, while Monte Carlo simulation and scenario analysis were applied to predict the agricultural non-CO2 GHG emissions from 2021 to 2035. Results reveal that non-CO2 GHG emissions from Chengdu's agriculture exhibit fluctuations and declines, with N2O emissions from agricultural land being the most substantial source. Driving actors such as emission intensity (EI), agricultural industrial structure (IS), and rural population (RP) contribute to carbon emission reductions, resulting in reductions of 8.10, 4.25, and 0.91 million tons (Mt), respectively. Conversely, the regional economic development level (EDL), and urbanization rate (URB) are the primary drivers behind increased agricultural non-CO2 GHG emissions, leading to emissions of 8.91 and 2.35 Mt, respectively. Ultimately, the predictive analysis demonstrates that, under the technological breakthrough scenario, Chengdu's agricultural non-CO2 GHG emissions are projected to peak in 2025, with an expected value of only 2.28 Mt. This study offered practical guidance for mitigating non-CO2 GHG emissions in city-level agriculture.

Agri-food evolution and carbon emissions in Chinese residential consumption: A life cycle analysis of urban-rural disparities and socioeconomic influences

At present, residential food consumption is not only the main driver of global energy expenditure but also a crucial factor shaping the development of the agri-food system. It is guided by the macro-food paradigm and serves as a cornerstone for ensuring comprehensive food security. This study examines both direct and indirect carbon emissions stemming from urban and rural food consumption in China between 2000 and 2021, employing a life cycle perspective. The research also delves into the associated factors influencing these emissions. Through the utilization of the LMDI (Logarithmic Mean Divisia Index) decomposition method, the study estimates the contributory significance of total carbon emissions attributed to residents' food consumption. Furthermore, the investigation undertakes an in-depth analysis of the disparities in the compositional makeup of food consumption among urban and rural populations, along with an exploration of the pertinent driving forces behind these distinctions. The study's outcomes indicate that, overall, there is a reduction in per capita direct carbon emissions, accompanied by a corresponding rise in per capita indirect carbon emissions. Relative to the index of plant-based food consumption, both the carbon emissions associated with, and the proportion of animal-based food consumption exhibited an ascending trend. The carbon dioxide (CO2) emissions stemming from the dietary patterns of urban inhabitants notably surpass those attributed to their rural counterparts, with a discernible and widening disparity between the two cohorts. Within the context of the life cycle analysis, consumption and production serve as pivotal components of the intricate network of indirect carbon emissions. Notably, the proportion of carbon emissions attributed to urban residents surpasses that of their rural counterparts in both the domains of food production and the subsequent phases of processing and transportation. Owing to the irrational configuration of cooking energy utilization among rural inhabitants, direct carbon emissions within the food consumption continuum, as well as carbon emissions derived from cooking energy usage, surpass corresponding metrics among urban dwellers. This discrepancy can be attributed to the unsystematic nature of the cooking energy framework prevalent in rural contexts. Population growth, per capita food consumption and changes in the structure of food consumption are important factors driving the growth of total food carbon emissions. Within this context, an augmentation in the intake of vegetables, alongside desiccated fruits, and vegetables, exhibits the potential to notably curtail carbon emissions emanating from the dietary practices of rural denizens. Similarly, an elevation in egg consumption emerges as a key determinant in substantially mitigating the carbon emissions stemming from the dietary patterns observed among urban inhabitants. The carbon emissions originating from both animal-based and plant-based food consumption among urban and rural populations are substantially influenced by the intricate interplay of factors such as the demographic composition and the per capita disposable income within both these strata of society.

Decoupling for Carbon Neutrality: An Industrial Structure Perspective from Qinghai, China over 1990–2021

Carbon neutrality is urgent as rapidly emerging economies aggravate their share of global energy demand. In China, the energy structure is dominated by fossil fuels, but it varies significantly across provinces. As an indicator of carbon neutrality, previous studies of decoupling between carbon dioxide emissions and economic growth focused at the national and sector levels in China. However, they overlook the role of industrial structure in decoupling at the provincial level. In this light, the following paper focuses on Qinghai Province, analyzing decoupling and its influencing factors for achieving carbon neutrality from an industrial structure perspective over 1990–2021. It uses the Tapio decoupling model to evaluate decoupling states and the Logarithmic Mean Divisia Index decomposition to evaluate the influencing factors. A Data Envelopment Analysis model of super-efficiency Slacks-Based Measure is used to evaluate the decarbonization efficiency. The study finds that the overall trend shifted from weak to strong decoupling. Strong decoupling dominated the primary industry while weak decoupling dominated the secondary and tertiary industries. Economic growth negatively impacted overall decoupling, while population had a marginal effect. Energy structure and intensity generally promoted decoupling. Additionally, the overall mean efficiency of decarbonization was 0.95, led by the tertiary industry. The paper concludes by discussing policy implications.

Relationship Between Carbon Emissions and Economic Development of Industrial Parks Based on Decoupling Index

Industrial parks are the main carriers of industrial activities and are also key areas for carbon emissions. To deeply explore the decoupling state of carbon emissions and economic development of industrial parks and the driving forces, Zhengzhou Economic Development Zone were taken as example, based on the energy consumption data of industrial enterprises above a designated size from 2011 to 2020, the IPCC carbon emission accounting method, Tapio decoupling model, and logarithmic mean Divisia index decomposition method were used to analyze the characteristics of carbon emissions from energy consumption in the park, the relationship between carbon emissions and economic development, and the driving factors of decoupling. The results showed that:① in terms of carbon emission characteristics, the carbon emissions of energy consumption in Zhengzhou Economic Development Zone were mainly indirect carbon emissions, and the total carbon emissions showed a trend of rapid growth in the early stage, slowing down in the medium term, and negative growth in the later stage. The carbon emission intensity was decreasing annually. ② From the perspective of decoupling, the decoupling index between total carbon emissions and economic development in Zhengzhou Economic Development Zone from 2011 to 2016 was 1.021, which was in a state of growth linkage, and the decoupling index decreased to 0.089 from 2016 to 2020, turning into a weak decoupling state. ③ From the analysis of driving factors, from 2011 to 2016, four factors, namely carbon emission coefficient, energy efficiency, industrial structure, and economic level, all had a restraining effect on the decoupling of carbon emissions in Zhengzhou Economic Development Zone, and from 2016 to 2020, they all turned into promotion except for the economic level. This study showed that among the factors for the decoupling of carbon emissions in the Zhengzhou Economic Development Zone, the economic level played a major inhibitory role, and energy efficiency played a major role in promoting it. The results of this study can provide a reference for the industry-city integrated industrial park represented by Zhengzhou Economic Development Zone to formulate corresponding carbon emission reduction policies and achieve the carbon peaking and carbon neutrality goals.

Forecasting urban carbon emissions using an Adaboost-STIRPAT model

Solving outstanding environmental issues, reducing carbon emissions, and promoting green development are necessary ways to achieve carbon neutrality and carbon peak goals. It is also an important issue faced by society today. This paper uses the Kaya identity combined with the logarithmic mean Divisia index (LMDI) decomposition method to analyze the factors affecting carbon emissions, and uses the Pearson correlation coefficient to screen out eight highly correlated features to construct an extended STIRPAT model. In order to further improve the accuracy of the model in predicting carbon emissions, this paper introduces the Adaboost algorithm from machine learning to enhance the STIRPAT model. Finally, scenario analysis is used to predict and analyze carbon emissions in Shandong Province from 2020 to 2050. The results show that: 1) The main factors affecting urban carbon emissions from 1998 to 2019 are economic growth effects, followed by energy structure effects and energy consumption effects. 2) Under three different development scenarios, Shandong Province can achieve carbon peak between 2030–2035, but there are differences in peaking time and peak values.

Carbon Emission Prediction and the Reduction Pathway in Industrial Parks: A Scenario Analysis Based on the Integration of the LEAP Model with LMDI Decomposition

Global climate change imposes significant challenges on the ecological environment and human sustainability. Industrial parks, in line with the national climate change mitigation strategy, are key targets for low-carbon revolution within the industrial sector. To predict the carbon emission of industrial parks and formulate the strategic path of emission reduction, this paper amalgamates the benefits of the “top-down” and “bottom-up” prediction methodologies, incorporating the logarithmic mean divisia index (LMDI) decomposition method and long-range energy alternatives planning (LEAP) model, and integrates the Tapio decoupling theory to predict the carbon emissions of an industrial park cluster of an economic development zone in Yancheng from 2020 to 2035 under baseline (BAS) and low-carbon scenarios (LC1, LC2, and LC3). The findings suggest that, in comparison to the BAS scenario, the carbon emissions in the LC1, LC2, and LC3 scenarios decreased by 30.4%, 38.4%, and 46.2%, respectively, with LC3 being the most suitable pathway for the park’s development. Finally, the paper explores carbon emission sources, and analyzes emission reduction potential and optimization measures of the energy structure, thus providing a reference for the formulation of emission reduction strategies for industrial parks.

Analysis of the decoupling effect and driving factors of carbon emissions from the transportation sector in Guangdong Province

To propel the green transformation of the transportation industry in Guangdong Province. Against the backdrop of the five-year plan, this study employs the Tapio decoupling model and the Logarithmic Mean Divisia Index decomposition method to analyze the decoupling status and driving factors of carbon emissions from transportation and economic growth in Guangdong Province from 2001 to 2020. The results show that:(1) Both the total volume of carbon emissions from transportation and per capita emissions exhibit an overall upward trend, with petroleum-based emissions accounting for an overwhelming 96%, with diesel emissions register the highest proportion and most substantial increase. (2) The decoupling status predominantly manifests as weak decoupling, with intermittent years expansive coupling,, expansive negative decoupling, strong decoupling, and recessive coupling, thus indicating a persistent state of decoupling instability. (3) The study introduces the urbanization effect, wherein the effects of income urbanization, as well as spatial urbanization, predominantly exert positive driving influences. Conversely, the effects of energy intensity, population urbanization, industry scale, and energy structure collectively exert negative driving influences. Notably, the effect of income urbanization emerges as the primary factor propelling the augmentation of carbon emissions, while the energy intensity effect serves as the primary factor curbing such escalation. Finally, pertinent policy recommendations are put forth.

Methodology for selecting assessment periods of Logarithmic Mean Divisia Index decomposition techniques

Logarithmic Mean Divisia Index Decomposition Analysis (IDA-LMDI) is a widely used statistical technique in the engineering, economics, energy, and environmental sciences. The main application of the IDA-LMDI method is to identify the drivers that explain the change in carbon dioxide emissions of a country or region over a given time span. Therefore, proper selection of each time period is fundamental to ensure that its implemented accurately and meaningful results are obtained. In the literature, decomposition periods have been defined based on a single-, n-, or one-year period. However, in all these cases, the duration of each period was fixed and depended on arbitrary criteria. The adoption of fixed periods did not capture specific changing trends in the time series under analysis. Therefore, this study presents a new methodology for defining the number of periods and their extent to obtain unbiased drivers using the IDA-LMDI technique. The period selection was defined according to the mean square error of all feasible combinations. We illustrated the application of this method in two cases. First, an illustrative example was successfully validated using multivariate linear regression. Second, we obtained the Kaya factors of the Organisation for Economic Co-operation and Development countries of Europe. The results were compared with the decomposition results provided by the International Energy Agency on 10–7 year fixed periods. The results showed that the application of fixed periods in IDA-LMDI dismissed critical drivers that could only be captured with the proper selection of each period under analysis. Therefore, the proposed methodology will aid in accurate information analysis and decision making in energy policy and other applications. The proposed method has a broad application and could be applied to any decomposition method in the Divisia family.