Logarithmic Mean Divisia Index Research Articles

Decomposition of the site-level energy consumption and carbon dioxide emissions of the iron and steel industry.

Identifying the key factors influencing energy consumption and CO2 emissions is necessary for developing effective energy conservation and emission mitigation policies. Previous studies have focused mainly on decomposing changes in energy consumption and CO2 emissions at the national, regional, or sectoral levels, while the perspective of site-level decomposition has been neglected. To narrow this gap in research, a site-level decomposition of energy- and carbon-intensive iron and steel sites is discussed. In this work, the logarithmic mean Divisia index (LMDI) method is used to decompose the changes in the energy consumption and CO2 emissions of iron and steel sites. The results show that the production scale significantly contributes to the increase in both energy consumption and CO2 emissions, with cumulative contributions of 229.63 and 255.36%, respectively. Energy recovery and credit emissions are two key factors decreasing site-level energy consumption and CO2 emissions, with cumulative contributions to the changes in energy consumption and CO2 emissions of -158.30 and -160.45%, respectively. A decrease in energy, flux, and carbon-containing material consumption per ton of steel promotes direct emission reduction, and purchased electricity savings greatly contribute to indirect emission reduction. In addition, site products and byproducts promote an increase in credit emissions and ultimately inhibit an increase in the total CO2 emissions of iron and steel sites.

Assessment of Water Resource Utilization and Analysis of Driving Factors in Zhoushan City Based on Water Footprint Theory and LMDI Model

Accurately analyzing the water system’s status in a region is crucial for mitigating water resource constraints on the development of island areas. This study centers on Zhoushan, an archipelago city, and employs the water footprint theory (WFT) and the logarithmic mean Divisia index (LMDI) model to scrutinize the evolution of water footprints from 2010 to 2020. It also dissects the impacts of economic, technological, and population factors on these changes. The findings are as follows: From 2010 to 2020, Zhoushan’s total water footprint has remained relatively stable, but its structure is imbalanced, indicating an unhealthy development. The agricultural water footprint dominates, though its proportion has steadily declined from 64% to 28%, while the imported water footprint has significantly increased from 2% to 29%. The skewed distribution of internal and external water consumption contributes to this imbalance, with internal water use accounting for a staggering 86.43%. The ecological security of water resources appears bleak, with average water scarcity and pressure indices reaching 0.42 and 0.41, respectively, indicating an overloaded state of water resources. Despite a 144.50% growth in water footprint efficiency from 2010 to 2020, the average water resource sustainability index merely stands at 0.531, and its sustainability remains precarious. The LMDI model results unveil that technological factors are the primary negative driving factors, contributing to 47.06% of the changes in Zhoushan’s water resource utilization. Conversely, economic and population factors play positive roles, contributing 42.29% and 10.65%, respectively. Therefore, in the course of development, there should be a focus on promoting water-saving tec-nologies, and continuously enhancing the efficiency of water resource utilization. Simultaneously, attention should be given to the development of the tertiary sector and the water resource pressure resulting from the mobile population, aiming to achieve sustainable water resource utilization and further ensure the ecological security of Zhoushan City.

Analysis of Green Transformation and Driving Factors of Household Consumption Patterns in China from the Perspective of Carbon Emissions

Over the years, the household sector has become the main source of carbon emissions. Thus, it is crucial to study the green transformation of household consumption patterns (HCPs) and its driving factors from the perspective of carbon emissions (CEs). This study used the Tapio decoupling method to analyze the green transformation of HCPs, employed the logarithmic mean divisia index (LMDI) method to find the driving factors of green transformation of HCPs, and conducted marginal effect analysis to identify the marginal effects of the major driving factors of green transformation of HCPs, based on the China Family Panel Studies (CFPS) database from 2012 to 2018. It was found through statistical analysis that dominant types of direct HCPs included electricity and fuel, and dominant types of indirect HCPs included necessities, health, transportation, and education. The results of empirical analysis indicated that direct household consumption structure and the per capita residential area promoted the green transformation of HCPs, while direct household per square meter residential consumption and per capita net income inhibited it. Furthermore, other factors had varying positive or negative impacts on the green transformation of HCPs, depending on regions, income levels, and urban–rural areas. The results of marginal effect analysis suggested that the marginal effects of residential area on per capita household carbon emissions (HCEs) present a trend from increasing to decreasing, while the marginal effect of household income on per capita HCEs presents an increasing trend. However, the marginal effect of household size on per capita HCEs presents a decreasing trend only for the low-income group and the western region when household size increased from five to six persons. This paper enriches the research on the green transformation of HCPs, and provides references for the formulation of green transformation policies for HCPs in different regions, income levels, and urban–rural areas.

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.

Socio-hydrology pathway of grain virtual water flow in China

As the world's largest grain producer, China's activities in virtual water trade for grain are particularly noteworthy. The imbalance of grain production and consumption among regions poses challenges to agricultural development and the sustainable use of water resources. Most of the previous research focused on the driving analysis of factors affecting agricultural water use at the production end. It is necessary to further explore the driving factors of virtual water flow (VWF) caused by grain trade on the consumption side, and consider the impact of production and consumption on the entire process of agricultural water use. This study combines the Logarithmic Mean Divisia Index (LMDI) index decomposition method and multiple regression model to analyze the main natural and social driving factors of VWF. The study also initiates the construction of an analytical framework for the dynamic interaction of coupled social hydrology and Metacoupling systems. The results indicate that economic growth is the primary driver behind the increase in VWF. The water intensity effect and industrial structure effect act as constraints on VWF. Notably, water intensity has the most significant negative effect, with contributions of − 118.9 Gm³ and − 192.4 Gm³ to the virtual water output and input areas, respectively. Conversely, the economic, demographic, and dependence effect play a promoting role. Among these, the positive economic effect far exceeds population and dependence effects, contributing 189.7 Gm³ and 231.3 Gm³ to the virtual water output and input areas, respectively. The analytical framework of coupled social hydrology and the dynamic interaction of Metacoupling systems reveals that VWF couples water resources with multiple systems through the production-consumption-pollution chain. Simultaneously, it achieves long-distance interactions across spaces through cross-space VWF, exhibiting socio-hydrological interactions. The input area displays a forward cycle, while the output area exhibits the opposite. These findings can serve as a theoretical reference for controlling grain VWF.

Analysis of carbon emission drivers and peak carbon forecasts for island economies

Sea islands are an essential support for expanding the blue economic space, and the study of the carbon emissions of Changhai County's island economies and their carbon peaking time is of great significance for achieving the goal of building an ecological low-carbon demonstration island with northern characteristics in Changhai County. In this study, the Logarithmic Mean Divisia Index (LMDI) method was used to explore the three main drivers affecting the carbon emissions of the island economies in Changhai County. The carbon emissions of Changhai County in 2012–2019 were estimated through the system dynamics model. The low-carbon development path and possible carbon peak time of Changhai County in 2020–2035 were predicted. The results show that: ①The urban system is the first positive driver of carbon emissions, followed by the residential system, and finally, the fishery system, the energy structure effect is the main positive driving effect affecting carbon emissions, and the population effect always shows a negative inhibitory impact on carbon emissions. ②The activities on the islands of Changhai County in 2012–2019 cumulatively produced 167.53 × 104 t of carbon emissions, 46.83 × 104 t of carbon sinks, and their net carbon emissions were 120.69 × 104 t. The results of the future scenario projections show that neither non-intervention nor a single policy of only adjusting land use and transport can achieve minor carbon emissions and that the best path for future low-carbon development in Changhai County is to develop all kinds of activities in a low-carbon (L) mode. ④Among the future forecast scenarios, Scenario 5 is an ideal model for the fastest way to achieve carbon peak, which generates 318.39 × 104 t of carbon emissions, and the cumulative emission reduction reaches 26.37 × 104 t.

Paving the way for sustainable agriculture: An analysis of evolution and driving forces of methane emissions reduction in China

The agriculture sector in China faces the dual challenge of ensuring food security while reducing methane emissions. This study investigates the factors driving changes in methane emissions from rice cultivation, livestock manure, and enteric fermentation in the agricultural sector between 2010 and 2019. First, we use the extended Logarithmic Mean Divisia Index method to measure four effects—emission source structure, emission intensity, economic activity, and population effects. Second, Tapio's decoupling index is employed to analyze the association between export-induced methane emissions and economic growth over the entire period. The results show that (1) rice cultivation is the primary contributor to the overall increase in national CH4 emissions, particularly after 2014, while CH4 emissions from other sources have decreased; (2) economic activity is the key driver of rising methane emissions, while emission intensity has the opposite impact, albeit with a decreasing contribution; and (3) decoupling states exhibit weak and strong decoupling recently, indicating the preliminary performance of the transformation of farm management. Furthermore, we find heterogeneous and spatially imbalanced performance in CH4 emission mitigation among provinces. Although China is on the right track to reduce CH4 emissions and upgrade the domestic and international agricultural structure, this has not been achieved at the provincial level.

Net-zero energy transition in ASEAN countries: The evolutionary model brings novel perspectives to the cooperative mechanism of climate governance

In an era marked by escalating climate change, the fragile ecological balance faces increasing strain. Whilst significant knowledge exists regarding the accumulation of carbon emission within the Association of Southeast Asian Nations, little is known about when and how countries could reach net-zero emission goal as agreed in Paris Agreement. For this purpose, our study examines the primary driving factors of carbon emission from 1990 to 2020 across the ten countries using the Logarithmic Mean Divisia Index model. We leverage the random forest model to explore the net-zero scenarios and the Autoregressive Integrated Moving Average approach to identify the evolutionary trajectories of carbon emission trends. Our findings underscore the imperative need for expediting decarbonization efforts, emphasizing the urgency for widespread adoption of clean technologies and substantial investment in green initiatives. Countries at similar stages of progress might establish a cooperation mechanism of clean energy base construction, energy storage allocation and policy formulation. These insights can help us better estimate future demand of clean energy, explore strategies for decarbonization, and inform historical commonalities of carbon emission growth.

Quantitative Study of Post-Pandemic Electricity Productivity Fluctuations Based on LMDI

This study focuses on China's electricity productivity post-pandemic, employing the Logarithmic Mean Divisia Index (LMDI) method and the Kaya identity. Despite the clarity of the electricity productivity indicator, factors like industrial structure, energy efficiency, and electrification rates contribute to fluctuations. Quantitative analysis reveals a decisive role of the terminal electrification rate in declining electricity productivity. The proposed analytical method provides a more accurate assessment of electricity's contribution to economic growth, aiding informed policy decisions for sustainable energy development in China.

Study on carbon emission driving factors and carbon peak forecasting in power sector of Shanxi province.

The realisation of the low-carbon transition of the energy system in resource-intensive regions, as embodied by Shanxi Province, depends on a thorough understanding of the factors impacting the power sector's carbon emissions and an accurate prediction of the peak trend. Because of this, the power industry's carbon emissions in Shanxi province are measured in this article from 1995 to 2020 using data from the Intergovernmental Panel on Climate Change (IPCC). To obtain a deeper understanding of the factors impacting carbon emissions in the power sector, factor decomposition is performed using the Logarithmic Mean Divisia Index (LMDI). Second, in order to precisely mine the relationship between variables and carbon emissions, the Sparrow Search Algorithm (SSA) aids in the optimisation of the Long Short-Term Memory (LSTM). In order to implement SSA-LSTM-based carbon peak prediction in the power industry, four development scenarios are finally built up. The findings indicate that: (1) There has been a fluctuating upward trend in Shanxi Province's total carbon emissions from the power industry between 1995 and 2020, with a cumulative growth of 372.10 percent. (2) The intensity of power consumption is the main factor restricting the rise of carbon emissions, contributing -65.19%, while the per capita secondary industry contribution factor, contributing 158.79%, is the main driver of the growth in emissions. (3) While the baseline scenario and the rapid development scenario fail to peak by 2030, the low carbon scenario and the green development scenario peak at 243,991,100 tonnes and 258,828,800 tonnes, respectively, in 2025 and 2028. (4) Based on the peak performance and the decomposition results, resource-intensive cities like Shanxi's power industry should concentrate on upgrading and strengthening the industrial structure, getting rid of obsolete production capacity, and encouraging the faster development of each factor in order to help the power sector reach peak carbon performance.

Inter-provincial factors decomposition and decoupling analysis of generalized agricultural carbon emissions in China

China, a country with a long-standing agricultural legacy, is increasingly prioritizing the reduction of CO2 emissions from its agricultural sector. Initially, the carbon emission sources within the agricultural sector are classified into two categories: direct and indirect emissions. Using this classification, the study calculates the generalized agricultural carbon emissions (GACEs) of 30 provinces in China between 2011 and 2020. To further understand the factors influencing GACEs, the paper employs the logarithmic mean Divisia index method and Tapio decoupling index to analyze seven key factors. These factors include carbon emission intensity, energy consumption of generalized agriculture, and economic benefit level of energy consumption. By comparing the impact and changes of GACEs during the 12th and 13th five-year plan periods, the study reveals valuable insights. The findings suggest that carbon emission intensity plays a crucial role in suppressing GACEs, while the level of economic development acts as a catalyst for their increase. By effectively managing these influencing factors, the paper proposes that the increase in GACEs can be effectively suppressed, and the achievement of agricultural CO2 reduction goals can be expedited.

Electricity generation and CO2 emissions in China using index decomposition and decoupling approach

Over the past three decades, energy and economic growth have been joined by enormous environmental pollution and growing global concerns. It is necessary to check the factors' effects impacting CO2 emissions and decouple CO2 from economic growth for the biggest emitter China. This study uses the logarithmic mean Divisia index extended by introducing electricity substitution factors (i.e., activity, population, electricity intensity, electricity overall, generation structure, energy efficiency, and fuel emission factor effects) and is then combined with Tapio's decoupling method to analyze the CO2 emission drivers, states and sectorial emissions for the years 1991–2020. The findings show that: (1) population and activity effects are the main driving factors in increasing CO2 emissions by adding trade and electricity generation structure effects. (2) Decoupling states presented the two decoupling states through electricity CO2 emissions and economic growth effects, showing that expansive negative decoupling is dominant. This shows that both factors show an increasing return to scale. (3) Individual factors and sectorial decoupling indexes show long-run variations and relationships between them, which means that industrial structure adjustment will help mitigate CO2 emissions and sustain economic development. Finally, based on empirical findings, the results suggest more ambitious targets for emerging low-carbon technologies that could help the rapid decarbonization of China's electricity sector.

The Effects and Driving Factors of Low-Carbon Transition of International Oil Companies: Evidence from a Super-SBM Model

As the main source of energy supply and carbon emissions, the oil and gas industry has entered the comprehensive low-carbon transition stage driven by various factors. Since different oil companies possess distinct understandings of transition paths, the effect of low carbon transition varies greatly. Obviously, it is necessary to evaluate the performance of low-carbon transitions within the oil and gas industry. Therefore, in this paper, 10 major international oil companies are taken as examples, and a super-efficiency slack-based measurement (super-SBM) model is applied in the present study to analyze the efficiency of low-carbon transitions. Furthermore, the logarithmic mean Divisia index (LMDI) is used to decompose the driving factors of carbon emissions and analyze their impact on the low-carbon transition of international oil companies. The obtained results reveal that although major oil companies have taken different measures in low-carbon transition and achieved a year-on-year reduction in carbon emissions, from the perspective of the efficiency of the entire production and operation process, these oil companies are inefficient in carbon emissions and need to adopt more effective low-carbon transition strategies; Moreover, after the further decomposition of the carbon emission driving factors of the 10 companies, it is found that improving the energy consumption intensity and development level of oil companies can effectively improve the effect of low-carbon transition of international oil companies. Drawing on the above findings, this paper puts forward suggestions for the low-carbon transition of energy companies, thus providing theoretical support and guidance for energy companies in different countries to implement low-carbon transition and green development strategies.

Analysis and Prediction of Factors Influencing Carbon Emissions of Energy Consumption Under Climate Change

Climate change is one of the major challenges currently facing the world. The factors influencing the carbon emission of energy consumption and the future trend are important guidance for proposing scientific carbon reduction strategies to mitigate climate change. In this paper, the Logarithmic Mean Divisia Index (LMDI) model and stochastic impacts by regression population, affluence and technology (STIRPAT) model are established to analyze and predict the carbon emission of energy consumption. The LMDI model is used to factorize the CO2 changes generated by residential domestic energy consumption, and to decompose and analyze the carbon emission factors of residential domestic energy consumption in terms of energy carbon emission intensity, energy consumption structure, energy consumption intensity, economic development, and population to determine the driving factors leading to carbon emission changes; based on the above study, we set up nine different development scenarios and applied the scalable stochastic environmental impact assessment model to project energy carbon emissions in 2035; based on carbon emission prediction and analysis, the CO2 emissions of total energy consumption, total electricity consumption, industrial energy consumption and terminal energy consumption were selected, and the correlation coefficients with relevant climate indicators such as temperature change and humidity change were analyzed, and the stress model of energy consumption on climate change was constructed. The results show that: the correlation coefficients of energy consumption indicators and temperature change indicators all pass the significance test at P = 0.01 level, among which the correlation coefficients with temperature difference are the highest, all of them are greater than 0.9 and pass the significance test at P = 0.001 level; among the indicators of energy consumption, the correlation coefficient between total industrial energy consumption and temperature difference was slightly higher than that of total energy consumption and electricity consumption; the stress relationship between the increase of energy consumption and the temperature difference is consistent with the growth of the third polynomial curve.

A Decomposition Analysis of Algeria’s Residential Energy Consumption Change: (2000–2020)

Between 2000 and 2020, the residential sector’s share of Algeria’s final energy consumption was 62.20% and 46.05% respectively; this was much higher than the global average of 31%. This enables us to comprehend the factors that contribute to the rise in energy consumption in the residential sector and to carry out a quantitative analysis to determine the extent to which energy efficiency reduces energy consumption in the sector between 2000 and 2020. To accomplish this, we employ the decomposition analysis approach by using the Logarithmic Mean Divisia Index (LMDI). The results of the study showed that the energy subsidy factor had a strong impact on increasing residential energy consumption in Algeria, compared to the population growth factor. In addition, the energy intensity factor and the economic structure factor did not help reduce energy consumption levels. According to these findings, Algerian public policymakers should strictly implement the national energy program Horizon 2030 by rationalising energy use, eliminating subsidies gradually, and monitoring the implementation of energy efficiency measures in the residential sector.

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.

Decomposition Analysis of Regional Electricity Consumption Drivers Considering Carbon Emission Constraints: A Comparison of Guangdong and Yunnan Provinces in China

Electricity consumption is closely linked to economic growth, social development, and carbon emissions. In order to fill the gap of previous studies on the decomposition of electricity consumption drivers that have not adequately considered carbon emission constraint, this study constructs the Kaya extended model of electricity consumption and analyzes the effects of drivers in industrial and residential sectors using the Logarithmic Mean Divisia Index (LMDI) method, and empirically explores the temporal and spatial differences in electricity consumption. Results show that: (1) During 2005–2021, the total final electricity consumption growth in Guangdong was much higher than that in Yunnan, but the average annual growth rate in Guangdong was lower, and the largest growth in both provinces was in the industrial sector. (2) The labor productivity level effect is the primary driver that increases total final electricity consumption (Guangdong: 78.5%, Yunnan: 87.1%), and the industrial carbon emission intensity effect is the primary driver that decreases total final electricity consumption (Guangdong: −75.3%, Yunnan: −72.3%). (3) The year-to-year effect of each driver by subsector is overall positively correlated with the year-to-year change in the corresponding driver, and declining carbon emission intensity is a major factor in reducing electricity consumption. (4) The difference in each effect between Guangdong and Yunnan is mainly determined by a change in the corresponding driver and subsectoral electricity consumption. Policy implications are put forward to promote energy conservation and the realization of the carbon neutrality goal.

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.

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.

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.