Logarithmic Mean Divisia Index Method Research Articles

The synergistic decarbonization potential from construction industry and upstream sectors with a city-scale: A case study of hangzhou, China

How to reduce Carbon Emissions from the Construction Industry (CECI) is one of the key issues in China's efforts to achieve its “dual-carbon target”. The Carbon Emission of the building Materialization Process (CEMP) is the main source of CECI, originating from both the upstream sector and the construction industry itself. Researching the synergistic decarbonization potential and reduction pathways of the construction industry and upstream sectors can help guide sustainable development. Focusing on the city scale, this study establishes a bottom-up framework based on system dynamics and carbon intensity methods to explore the possibilities of CEMP. The framework comprises a dynamic material flow analysis module and a carbon emission accounting module. The Logarithmic Mean Divisia Index (LMDI) method is employed to elucidate the emission reduction potentials of various factors. The results show that in the case of Hangzhou: (1) Without control measures, CEMP will reach a maximum of 25.1 million tons, and a substantial scale of 14.9 million tons remaining in 2060. (2) Through synergistic decarbonization, the cumulative emission reduction over the next 40 years could reach 323.4 million tons, and by 2060, CEMP could decrease to 3.7 million tons of CO2. Achieving carbon neutrality in the construction industry still requires carbon offsets from other sectors. (3) The decarbonization potential mainly comes from controlling the new construction area and carbon emission reduction in material production process, contributing 45.4% and 36.9%, respectively. This study reveals the significant pressure faced by the construction industry in achieving carbon neutrality. It also underscores that further controlling material demand and reducing the carbon intensity of materials is a prerequisite for Hangzhou's construction industry to achieve carbon neutrality.

Towards customized mitigation strategy in the transportation sector: An integrated analysis framework combining LMDI and hierarchical clustering method

As the world's largest CO2 emitter, China's decarbonization in the transportation sector is vital for achieving global sustainable goals. This study develops an in-depth framework to elucidate and tailor decarbonization strategies across regions effectively. Through the application of the Logarithmic Mean Divisia Index (LMDI) approach, this analysis spans transportation, energy, economic, and demographic factors influencing CO2 emissions in China's 30 provinces over the decade 2010–2019. The Entropy Weight Method (EWM), applied innovatively, pinpoints regional disparities in emission drivers. A dual-dimensional hierarchical clustering analysis evaluates the emission characteristics at provincial-level, laying the groundwork for customized mitigation strategies. The study delineates three principal discoveries: (1) robust economic development has spurred transportation demand over the past decade, escalating emissions. While initiatives like the "Beautiful China" campaign and decarbonization policies have fostered weak decoupling between economic growth and emissions, economic activity remains a dominant driver of emissions at the provincial level. (2) The energy intensity, freight intensity, and industrial structure are the primary sources of regional disparities in the driving mechanism of CO2 emissions in the transportation sector. In economically developed areas, freight intensity is a considerable emissions accelerator, marking a departure from its national-level emissions reduction role. Given the starkly distinct regional disparity, a “one-size-fits-all” approach for China would be imprudent. (3) The novel application of hierarchical clustering reveals three synergistic relationships between regional development and transportation decarbonization, highlighting the misalignment between emissions reduction potential and driving forces in 27 provinces. This misalignment underscores the inefficiency of current management practices, necessitating the development of precise tailored strategies. By pioneering in the methodological analysis of emissions dynamics and stressing the imperative of customized mitigation strategies, this research significantly enriches the discourse on sustainable transportation and regional policy innovation.

The race between global economic growth and carbon emissions: based on a comparative study of developed and developing countries.

In recent years, there has been a persistent intensification of the global greenhouse effect. Balancing carbon emission reduction with economic growth poses an unprecedented global challenge. To better comprehend the relationship between economic growth and carbon emissions, this study first utilized the Tapio decoupling index to compare the decoupling relationship (the USA, Japan, and Germany) and three developing countries (China, India, and Russia) from 2000-2020. Additionally, the logarithmic mean Divisia index (LMDI) method was employed to investigate the factors influencing changes in carbon emissions. Our findings indicate that (1) the USA and Germany basically achieved strong decoupling; China, India, and Russia mainly showed weak decoupling; and Japan showed recessive decoupling. (2) Economic growth predominantly contributed to increased carbon emissions, with a lesser impact from population growth. A significant reduction in energy intensity restrained carbon emissions growth, as did energy structure replacement in most countries, excluding Japan. Based on this, a decoupling effort index was formulated. It has shown that the decoupling efforts made by developing countries are weaker than those of developed countries, primarily attributed to a lesser degree of decoupling between energy intensity and structure. This paper offers valuable insights for developing countries undergoing a low-carbon economic transformation. They should counterbalance carbon emission escalation resulting from economic growth through technological and energy structure improvements.

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.

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.

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.

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.

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.

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.

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.

Coal efficiency, carbon reduction, and future policy perspective in Pakistan’s economic growth: a decomposition and decoupling approach

Pakistan is moving toward the large-scale use of coal. Coal plays a dominant role in Pakistan’s energy mix and is estimated to reach 30% by 2030. The purpose of this study is to analyze coal imports and indigenous reserves in relation to CO2 emissions. In particular, this study constructs the logarithmic mean Divisia index (LMDI) method to see the impact of the factors, decoupling index for the economic relationship, and pollution from coal-fired power plants from 1986 to 2019. The empirical results show that 1) coal consumption and imports are interrelated, while coal production has had the lowest production level since 1986; 2) the energy intensity impact plays a medium role in decreasing coal utilization, followed by the coal share effect; however, the aggregated impact accounts for 0.023% of the total coal use; 3) the economic and population activity effects progressively increase with coal consumption by 0.25% and 0.35%, respectively, with the annual average growth; 4) only “three” decoupling states were observed: expansive coupling, expansive negative decoupling, and weak decoupling. Expansive negative decoupling occurred due to high energy share and energy intensity. Expansive coupling occurred only in 2001 due to rapid growth in coal proportion and a sluggish decrease in energy intensity, and weak decoupling showed a decoupling association between economic growth and coal utilization; and 5) the various coal compositions, such as moisture, volatile matter, fixed carbon, ash, and sulfur, can be evaded by 1.82, 4.83, 5.16, 1.43, and 0.39 Mt currently. Finally, environmental analysis recognized that implementing clean coal technologies significantly saves fuel and, consequently, reduces emissions. This study also discusses further policies.

Decoupling tourism growth from carbon emissions: Evidence from Chengdu, China.

The fast growth increases in energy use and greenhouse gas emissions in China's tourism industry. This article assessed the carbon emissions of tourist Chengdu's traffic patterns and how they are expected to change between 2005 and 2021 and explains why tourism carbon decoupling is important for sustainable tourism development. In order to investigate how carbon emissions from tourism may expand without necessarily increasing in tandem with GDP and the variables that influence it, the Tapir model and the LMDI (logarithmic mean Divisia index) method were used. In our study, we looked at six important variables, including (1) the number of visitors, (2) the level of tourism spending per capita, (3) the contribution rate of the tourism sector with according to the country's total national income, (4) the amount of passenger traffic relative to GDP, and (5) the energy consumption relative to the volume of passenger traffic. There have been five stages in the correlation: positively charged decoupling, weak decoupling, negative decoupling, strong coupling, and strong decoupling. All of these describe the relationship between the expansion of China's tourist industry and the country's carbon emissions. Findings highlighted the importance of large numbers of visitors, high levels of per-person tourism spending, and low passenger traffic volume per unit of energy consumption as key positive factors. The rise of carbon emissions may also be slowed by increasing the number of passengers transported per unit of GDP. Carbon emissions from tourists in Chengdu are fluctuating, and this influences the city's economy. The findings have significant theoretical and practical implications for Chengdu's transition to a low-carbon economy and for the formulation of policies to reduce emissions. During the research period, most Chinese provinces exhibited ideal decoupling situations. This research has the potential to be used as a scientific resource for guiding the long-term growth as a result of China's tourist sector.

Analysis of the Social and Economic Factors Influencing PM2.5 Emissions at the City Level in China

Respirable suspended particles (PM2.5) are one of the key components of haze, which not only causes a variety of lung, intestinal, and vascular diseases, but also affects cognitive levels. China is facing the challenge of severe PM2.5 concentrations, especially in urban areas with a high population density. Understanding the key factors that influence PM2.5 concentrations is fundamental for the adoption of targeted measures. Therefore, this study used the Logarithmic Mean Divisia Index (LMDI) method to identify the key factors influencing PM2.5 concentrations in 236 cities in northeastern, western, central, and eastern China. The findings were as follows. The emission intensity (EI) played an important suppressing role on PM2.5 concentrations in all cities from 2011–2020. The energy intensity (EnI) inhibited PM2.5 concentrations in 157 cities; the economic output (EO) stimulated PM2.5 concentrations in some less economically developed regions; and population (P) spurred PM2.5 concentrations in135 cities, mainly concentrated in developed eastern cities. This study provides a whole picture of the key factors influencing PM2.5 concentrations in Chinese cities, and the findings can act as the scientific basis and guidance for Chinese city authorities in formulating policies toward PM2.5 concentration reduction.

Analysis of energy-related CO2 emissions in Pakistan: carbon source and carbon damage decomposition analysis.

Carbon dioxide emissions (CO2es) are presently a hot topic of worldwide concern. It is of great significance for lessening CO2es to wholly understand the transformation pattern of CO2es among countries, industries, and the main factors (i.e., emission effect, energy intensity, economic development, population size, carbon per unit of land, land per capita, and environmental impact per capita effects) influencing CO2es. Thus, to mitigate the country's CO2es efficiently, it is necessary to determine the driving factors of its emissions and damage variations. For this, we use the logarithmic mean Divisia index method. This research decomposes the major two dimensions, such as carbon sources and carbon damage variations from 1986 to 2020, into eight factors. The results show that Pakistan's CO2es increased continuously during the period, with an average annual growth rate of 4.76%. Growing the country's CO2es over 1986-2020, the key influencing factors are economic development, population, and land, while energy intensity and emission factors are the main forces in mitigating CO2es. The carbon source and carbon damage dimensions reached 68.75 Mt and 208.56 Mt, respectively, which led to a rise in CO2e. The entire set of factors is averagely moving around the major outcomes that provide significant policy measures. Finally, to efficiently reduce CO2e, Pakistan should concentrate on specific industrial paths and implement challenging, comprehensive governance to attain a low-carbon chain throughout the process. Thus, based on empirical results, this research put forward policy suggestions for cleaner production to reduce CO2 emissions further, and environmental policies must be tailored to local conditions.

Analysis of Ningxia Hui Autonomous District’s Gray Water Footprint from the Perspective of Water Sustainability

Gray water footprint (GWF) is an effective method to evaluate the degree of water pollution and water quality. It is the amount of freshwater needed to dilute water pollutants to meet ambient water quality standards. Accounting and analyzing the GWF will be significant for promoting an improved water environment and sustainable water ecology in Ningxia Autonomous District. We accounted for the GWF of all cities in Ningxia from 2012 to 2020 and evaluated its spatial-temporal variations by the GWF accounting method proposed by Hoekstra. Then, the Logarithmic Mean Divisia Index (LMDI) method was applied to investigate the contributions of four driving factors: the population scale effect, economic development effect, technological effect, and industrial structure effect. And then, the changes in the GWF in the Ningxia region were analyzed. The results showed that the GWF in the Ningxia region changed from 79.21 × 108 to 29.09 × 108 m3/yr during 2012–2020, making a significant decreasing trend. Among all cities, Wuzhong City contributes the most in terms of the GWF. More specifically, economic development and technology structure are the positive and negative drivers of the GWF, respectively. The water pollution levels in Ningxia (0.49–1.3) indicated that the waste assimilation capacity has fallen short of taking up the pollutant load, which had an unfavorable impact on the groundwater according to actual water quality data. NO3-N and NH3-N are detected in the groundwater throughout the Ningxia region, with the highest NH3-N content in the groundwater in Yinchuan, which almost exceeded the groundwater quality standard of category III. Above all, this study reflected the current water pollution situation better by combining the GWF with actual water quality data in Ningxia. The finding of this study is valuable for addressing water quality threats and developing sustainable development.

An Analysis of the Driving Factors Related to Energy Consumption in the Road Transport Sector of the City of Douala, Cameroon

The city of Douala in Cameroon is facing great challenges in terms of its demographic growth, economic development and urbanization, especially in relation to environmental and economic factors. However, there has been significant growth in its road transport sector, which has led to an excessive demand for the consumption of fossil fuels and an increase in greenhouse gas emissions in recent decades within this sector. However, no concrete policy has yet been put in place to improve the energy efficiency of the transport sector. This work aims to identify the driving factors and determine their contributions to the variation in energy consumption. In this study, a decomposition analysis via the Logarithmic Mean Divisia Index (LMDI) method is used for the period of 2010–2019 to quantify the respective effects of the driving factors on the variation in energy consumption. Based on the study of the literature, we classified four main driving factors in the road transport sector that contributes to the total variation in energy consumption, such as vehicle energy intensity, vehicle intensity, gross domestic product (GDP) by capita, and population scale, with each contributing 13.06%, 31.30%, 12.85%, and 42.76%, respectively. In particular, we note that the energy intensity coefficient of the vehicles from 2013 to 2016 and that of the intensity of the vehicles coefficient from 2010 to 2011 and 2012 to 2013 are the two factors that have, nevertheless, led to a slight decrease in the variation in energy consumption. This implies that an improvement in these two factors would contribute to enhancing the energy efficiency of the road transport sector of the city of Douala. It will therefore be necessary to put in place several energy-saving strategies that would lead to a rationalization of energy consumption in order to reduce greenhouse gas emissions by road transports. Policymakers should take this study into account to achieve a balance between energy consumption and economic growth to better integrate the notion of sustainable road transport.

Carbon footprints in pork production and consumption in China from 2005 to 2020

Livestock farming accounts for 14.5% of global total anthropogenic greenhouse gas (GHG) emissions. China has been a major contributor to global GHG emissions from livestock and is the biggest pork producer and consumer worldwide. However, those factors driving GHG emissions from pork production and consumption in China have not been thoroughly assessed. Here, we used the Global Livestock Environmental Assessment Model (GLEAM) and the Logarithmic Mean Divisia Index (LMDI) method to identify and assess the major drivers contributing to the life-cycle carbon emissions embodied in China's pork production and consumption from 2005 to 2020. Our results show that CO2 emissions accounted for 99% of the total GHG emissions in the pig industry, primarily released from the processing and transporting of pig feed. These two sectors contributed more than 90% to the total CO2 emission. CH4 was mainly emitted from enteric fermentation and manure management of pigs, which accounted for a tiny portion of total carbon emissions in the pork industry. Among the primary factors driving pork-induced carbon emissions in China, the reduction of pork emission intensity (PPE) significantly slowed down growing carbon emissions, attributed to the improvements in pork production technology and the promotion of green farming. Our finding reveals that the growing population primarily contributed to elevated carbon emissions before 2015 due to increasing demands for pork meat. Since 2015, the rapid growth of the aged population in China has decreased pork consumption capacity and carbon emissions from pork consumption. The result indicates, for the first time, that the population aging in China, the largest pork consumer in the world, would play a vital role in reducing pork consumption-induced carbon emissions. Such an effect would become more significant in the future, along with the growing aged population in China, favoring the country's GHG emission mitigation increasingly.

Assessment and prediction of net carbon emission from fishery in Liaoning Province based on eco-economic system simulation

The fishery eco-economic system has multiple economic-ecological-social values, and the marine fishery has the dual characteristics of "carbon source" and "carbon sink"; net carbon emissions are essential to agricultural production activities' carbon emissions. This is a breakthrough in achieving the goal of “carbon peaking and carbon neutrality goals.” This study defines the connotation of the fishery eco-economic system and innovatively proposes to combine the Logarithmic Mean Divisia Index (LMDI) method with the SD model to construct a dynamic model of net carbon emission of the fishery eco-economic system in Liaoning. In this study, the main drivers of carbon emissions from fishery were investigated by using the LMDI method, the net carbon emissions from the marine fishery in Liaoning Province from 2005 to 2020 were estimated by using a system dynamics model from the perspective of fishery ecosystem, and a multi-scenario projection of net carbon emissions from fisheries from 2021 to 2040 was made. The results show that: (1) The energy structure and energy intensity have a negative inhibitory effect on carbon emissions, and the fishery economy and the fishery population have a favorable driving effect on carbon emissions. (2) During 2005–2020, carbon emissions from fishery reached a maximum of 2.5 × 105t in 2010 and began to decline after that. Carbon emissions from marine fishing account for 60% of the total carbon emissions. (3) Across the five forecast scenarios from 2021 to 2040, rapid economic growth significantly affects carbon emissions growth, reducing energy use in fisheries through technological advancements helps to reduce carbon emissions, and increasing shellfish and algal production simultaneously increases carbon sinks.

Decomposition analysis on factors affecting electricity substitution in Guangdong province, China

AbstractElectricity substitution is an effective measure for reducing pollutant emissions and promoting the consumption of renewable energy. The current research lacks a quantitative analysis method for the factors affecting regional electricity substitution. This article proposes a decomposition model of the factors affecting electricity substitution based on Logarithmic Mean Divisia Index method to expand the depth and breadth of electricity substitution. Results show that: (1) Macro‐level electricity substitution in Guangdong province develops steadily, with 1694.93 × 108 kWh of total electricity substitution quantity during the period 2006–2020. The electricity substitution quantity in production sector accounts for 83.92% of the total, which is much larger than that in household sector. (2) Sustained improvement of labour productivity gives the largest contribution (3592.32 × 108 kWh) to the increase of electricity substitution in production sector during the period 2006–2020. Living standard effect has the largest contribution (452.21 × 108 kWh) to the increase of electricity substitution in household sector during the period 2006–2020. Electrification level effect and population effect have significant impact on electricity substitution development, while industrial structure has little impact on electricity substitution. (3) Energy intensity effect and energy consumption growth effect negatively influence electricity substitution in Guangdong province. The decline of energy intensity in the production sector has driven the decrease of electricity substitution quantity, contributing −2540.36 × 108 kWh to electricity substitution during the period 2006–2020. Policy implications like continuously promoting breakthroughs in electricity substitution technologies, improvement of electrification rate in the sectors with potential for substitution, and improvement the price mechanism for fossil fuels and electricity can promote the deepening development of electricity substitution and the realisation of the carbon neutrality goal.