Logarithmic Mean Divisia Index Research Articles

Financial levers and carbon emissions: analysing the debt-emission nexus in the European Union

ABSTRACT This paper examines the intricate relationship between the national debts of EU-27 member countries and their CO2 emissions, employing the Logarithmic Mean Divisia Index additive model to dissect the impacts of different financial and economic factors on environmental sustainability. To thoroughly explore the evolution of CO2 emissions and influencing factors, the 27 economies analysed were classified into two categories (advanced economies and emerging economies). Furthermore, three sub-periods for analysis were set (1995–2007, 2008–2016, and 2017–2022). The study identifies significant correlations between debt structures, economic growth, energy consumption patterns, and CO2 emissions. The findings reveal that Economic Growth is the largest contributor to the CO2 emissions increase, followed by Total Debt Level. A powerful and constant counterbalancing effect of CO2 emissions reduction is assured by Fossil Fuel Financing. The results underscore the complex interplay between financial and environmental policies in the EU, emphasizing the need for balanced approaches to achieve climate goals. It suggests that investment in green technologies and renewable energy sources, alongside innovative financing mechanisms for green technologies, enhanced energy efficiency, and fiscal policies aimed at reducing national debts, can have the dual benefit of mitigating climate change while also enhancing economic stability within the EU.

Energy consumption decomposition analysis using European official statistics: Methodology and input data

Deciphering energy efficiency is a critical component for the sustainability and energy policies of the European Union (EU) and its Member States. Decomposition analysis is a key method that helps distinguish real energy efficiency gains from other external factors. This article presents a decomposition analysis method using official EU statistics, its results, and associated limitations. Although separating structural changes and activity levels from energy efficiency poses a challenge, an adapted Logarithmic Mean Divisia Index (LMDI) method was utilised to isolate and highlight energy efficiency. This article outlines this method and how the European official statistics were exploited. Firstly, it examines the factors affecting energy consumption in various sectors within EU-27. Secondly, it examines the factors affecting energy consumption in various sectors. In doing so, it also addresses potential obstacles in data collection, and presents improvements to the LMDI analysis. The findings in this study make a substantial contribution to the fields of national statistics, methodological applications, and energy data analysis in the context of the EU’s energy policies.

Role of energy efficiency in energy transition: A decomposition analysis of energy use

The use of energy-efficient technologies tends to reduce the overall energy use of a country and foster the energy transition pathways. However, a change in energy can occur either due to a change in activity effect, intensity effect, or structural effect. The objective of the study is firstly to examine and measure the magnitude of change in energy use and identify the factor responsible for the change in energy use in the selected sectors. Secondly, the paper aims to analyze the impact of the Norwegian economy on the three effects of energy use. The study contributes significantly to identifying the sector that experiences a reduction in energy use due to energy efficiency and examining the impact of the economy on energy use. The overall energy use between 1990 and 2017 is decomposed into three different effects for selected energy-intensive sectors by deploying the ‘Logarithmic Mean Divisia Index’ (LMDI) method. Further, the impact of the Norwegian economy is examined on the three effects of energy use. It is observed that in each Phase, the key driver for change in energy use in all three Phases is the transport sector. Post-recession, the energy use in the transport sector was due to structural effects. Consumer behavior and limitations of sources of finance are the challenges for the deployment of electric vehicles even after technological breakthroughs in energy efficiency. Finally, the policies to enhance energy efficiency in energy-intensive sectors, such as the transport, and services sector must be undertaken to efficiently visualize energy efficiency-driven energy transition.

Efficiency and Driving Factors of Agricultural Carbon Emissions: A Study in Chinese State Farms

Promoting low-carbon agriculture is vital for climate action and food security. State farms serve as crucial agricultural production bases in China and are essential in reducing China’s carbon emissions and boosting emission efficiency. This study calculates the carbon emissions of state farms across 29 Chinese provinces using the IPCC method from 2010 to 2022. It also evaluates emission efficiency with the Super-Slack-Based Measure (Super-SBM model) and analyzes influencing factors using the Logarithmic Mean Divisia Index (LMDI) method. The findings suggest that the three largest carbon sources are rice planting, chemical fertilizers, and land tillage. Secondly, agricultural carbon emissions in state farms initially surge, stabilize with fluctuations, and ultimately decline, with higher emissions observed in northern and eastern China. Thirdly, the rise of agricultural carbon emission efficiency is driven primarily by technological progress. Lastly, economic development and industry structure promote agricultural carbon emissions, while production efficiency and labor scale reduce them. To reduce carbon emissions from state farms in China and improve agricultural carbon emission efficiency, the following measures can be taken: (1) Improve agricultural production efficiency and reduce carbon emissions in all links; (2) Optimize the agricultural industrial structure and promote the coordinated development of agriculture; (3) Reduce the agricultural labor scale and promote the specialization, professionalization, and high-quality development of agricultural labor; (4) Accelerate agricultural green technology innovation and guide the green transformation of state farms. This study enriches the theoretical foundation of low-carbon agriculture and develops a framework for assessing carbon emissions in Chinese state farms, offering guidance for future research and policy development in sustainable agriculture.

Recent CO2 emission and projections in Chinese provinces: New drivers and ensemble forecasting

Although extensive studies focused on the driver of changing CO2 emission, the roles of labor and capital were largely ignored in shaping spatiotemporal change in CO2 emission and forecasting differences on CO2 emission was few considered, hindering relevant policymaking towards sustainable development in both climate change mitigation and economic growth for developing countries in particular. To fill the gap above, the study explored the roles of capital and labor in contributing to recent CO2 emission in a case of China over 2010–2019 and projecting provincial CO2 emissions to 2030, by proposing two new spatiotemporal logarithmic mean Divisia index models with Cobb-Douglas production function and developing an ensemble forecasting model including machine learning. We found, first, the effects of capital and labor inputs and carbon factor were the positive drivers affecting aggregate CO2 emissions, while the effects of the total-factor productivity and energy intensity were negative drivers. Second, the effects of capital and labor inputs were the negative drivers for narrowing the emission gap. Third, the ensemble forecasting model can improve the generalization ability of CO2 emission predictions. Therefore, we recommend that policymakers focus on optimizing the carbon reduction effects of capital and labor inputs while promoting the development of a circular economy to achieve sustainable economic growth.

Evaluating the carbon emissions of Hong Kong's building sector from the life cycle perspective

The building sector is a typical end-use sector that involves multiple stages associated with carbon emissions (CE). Accurately quantifying the CE of the building sector from the life cycle perspective can establish a foundation for its decarbonisation. This study focuses on Hong Kong to develop a six-stage CE calculation methodology with the process-based life cycle assessment framework. Specifically, the datasets of newly-constructed buildings are analysed to classify the buildings into 11 archetypes. Furthermore, the logarithmic mean Divisia index is used to identify the primary driving factors of the CE. Results show that the CE of Hong Kong's building sector ranged from 34.75 to 40.23 Mt per annum during the ten-year period 2012–2021. Electricity consumption accounted for over 90 % of the operational CE, while the production of major building materials, such as steel, cement, and aluminium, took up over 80 % of the embodied CE. Moreover, energy efficiency and CE efficiency factors were revealed to impose significant impacts on CE decrease by 1.94 and 1.68 Mt cumulatively in 2012–2021, whereas the population factor was found to increase the CE by 1.43 Mt. The findings should inform Hong Kong building sector's decarbonisation plan, and the study provides an innovative approach for tracking the climate change impacts of building sectors in cities.

Decomposition and scenario analysis of agricultural carbon emissions in Heilongjiang, China.

As a key agricultural region in China, Heilongjiang Province has experienced significant carbon emissions over the past few decades. To understand the underlying factors and future trends in these emissions, a comprehensive analysis was conducted from 1993 to 2030. The agricultural carbon emissions from 1993 to 2020 were estimated using the emission factor method. To analyze the influencing factors and future trends of these emissions, the study employed the Logarithmic Mean Divisia Index (LMDI) and integrated it with the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model. Results showed that (1) the agricultural carbon emissions in Heilongjiang were primarily driven by rice cultivation, followed by fertilizer production and irrigation electricity. (2) The economic and labor structure effects were the main driving factors of agricultural carbon emissions, while the population, demographic, and intensity effects were the main inhibitors. (3) Agricultural carbon emissions in Heilongjiang Province peaked in 2016 with 69.6 Mt CO2-eq and could subsequently decline by -3.92% to -4.52% between 2020 and 2030 in different scenario simulations. In the future, Heilongjiang Province should prioritize the reduction of agricultural carbon emissions from rice production. Adjusting the planting structure, managing the layout of rice paddies, and promoting the cultivation of dry rice varieties would significantly contribute to mitigating agricultural carbon emissions.

The Carbon Footprint and Influencing Factors of the Main Grain Crops in the North China Plain

The North China Plain (NCP) serves as a critical grain-producing region in China, playing a pivotal role in ensuring the nation’s food security. A comprehensive analysis of the carbon footprint (CF) related to the cultivation of major grain crops within this region and the proposal of strategies to reduce emissions through low-carbon production methods are crucial for advancing sustainable agricultural practices in China. This study employed the lifecycle assessment (LCA) method to estimate the CF of wheat, maize, and rice crops over a period from 2013 to 2022, based on statistical data collected from five key provinces and cities in the NCP: Beijing, Tianjin, Hebei, Shandong, and Henan. Additionally, the Logarithmic Mean Divisia Index (LMDI) model was utilized to analyze the influencing factors. The results indicated that the carbon footprints per unit area (CFA) of maize, wheat, and rice increased between 2013 and 2022. Rice had the highest carbon footprint per unit yield (CFY), averaging 1.1 kg CO2-eq kg−1, with significant fluctuations over time. In contrast, the CFY of wheat and maize remained relatively stable from 2013 to 2022. Fertilizers contributed the most to CF composition, accounting for 48.8%, 48.0%, and 25.9% of the total carbon inputs for wheat, maize, and rice, respectively. The electricity used for irrigation in rice production was 31.8%, which was much higher than that of wheat (6.8%) and maize (7.1%). The LMDI model showed that the labor effect was a common suppressing factor for the carbon emissions of maize, wheat, and rice in the NCP, while the agricultural structure effect and the economic development effect were common driving factors. By improving the efficiency of fertilizer and pesticide utilization, cultivating new varieties, increasing the mechanical operation efficiency, the irrigation efficiency, and policy support, the CF of grain crop production in the NCP can be effectively reduced. These efforts will contribute to the sustainable development of agricultural practices in the NCP and support China’s efforts to achieve its “double carbon” target.

An Efficiency Evaluation and Driving Effect Analysis of the Green Transformation of the Thermal Power Industrial Chain: Evidence Based on Impacts and Challenges in China

The high carbon emissions and pollution of China’s thermal power industry chain have exacerbated environmental and climate degradation. Therefore, accelerating the green transformation process is of great significance in promoting the sustainable development of enterprises. This study selected 30 listed thermal power enterprises in China as research objects, analyzed their data from 2018 to 2022, set targeted input–output indicators for different stages, and used a two-stage dynamic data envelopment analysis (DEA) model to evaluate and measure the efficiency of the green transformation of Chinese thermal power enterprises. In addition, this study also uses the logarithmic mean Divisia index (LMDI) method to analyze the driving effects of green transformation. The results indicate that in terms of overall efficiency, there is a significant difference in the overall performance of these 30 thermal power enterprises, with a large difference in average efficiency values. Efficiency values are related to enterprise size. In terms of stage efficiency, the average efficiency value of thermal power enterprises in the profit stage was significantly higher than that in the transformation stage, and the profitability of Chinese thermal power enterprises was better. In terms of sub-indicator efficiency, the efficiency of each indicator shows a “U”-shaped trend, and there is a certain correlation between the operating costs and revenue of thermal power enterprises, the market value of green transformation, and related indicators. In addition, the most important factor affecting the efficiency of green transformation is the sewage cost they face, whereas their operational capabilities have the least impact on their green transformation. In this regard, thermal power enterprises should increase their investment in the research and development of key technologies for thermal power transformation and continuously optimize their energy structure. The government will increase financial support for thermal power green transformation enterprises and correspondingly increase emission costs.

Effect of water Resource utilization in Poyang lake area on carbon emissions based on decoupling theory

The utilization of regional water resources has the potential to impact carbon emissions. Maintaining a decoupled relationship between water resources and carbon emissions facilitates harmonious regional development. Understanding the mechanism of their coordination is conducive to achieving the "Double Carbon" goal and control of regional carbon emissions and water resource consumption. This study examines the decoupling relationship between water resource utilization and carbon emissions in Poyang Lake area, China, employing the Tapio decoupling model and the LMDI(logarithmic mean divisia index) decomposition model. The results indicate that carbon emissions in Poyang Lake area exhibited a gradual increase, accompanied by an annual growth rate of 5.99 %. The water supply exhibited a slow expansion. They have exhibited state of affairs strong negative decoupling and expansive negative decoupling over the past 15 years. Moreover, this situation is most acute and worsening in the secondary industry. The water use structure effect and water economic benefit effect are the primary factors affecting carbon emission increases, contributing 57.93 % and 65.66 %, respectively. Carbon emissions intensity is the largest inhibiting factor, which accounts for a maximum contribution of 42.96 %. The order of potency of the driving factors is as follows: water economic benefit > carbon emission intensity > water use structure > water use efficiency. In summary, this research recognised the enhancement of the water economic efficiency index not only facilitates the decoupling phenomenon but also improves the water-carbon relationship, especially in the secondary industry. It serves as a compelling illustration of the significance of elucidating the interrelationship between regional water and carbon dynamics, and charting the course for the formulation of regional policies that would facilitate the advancement of environmentally conscious and carbon-neutral development, as well as water conservation.

Ensuring China's food security in a geographical shift of its grain production: Driving factors, threats, and solutions

Grain production in China has increased over time despite limited access to natural resources. However, this has led to China's grain supply system becoming increasingly vulnerable and unsustainable due to a northward shift of its grain production. Here, we establish a framework to guide future grain policy formulation in response to previous practices and lessons learned from China's past grain production. This framework integrated the spatial gravity model, spatial mismatch index, logarithmic mean Divisia index (LMDI), and Monte Carlo simulation to identify the key drivers behind grain growth, analyze their impacts on food security, and propose countermeasures. We found that grain production increase in China is not universally attributable to increased input of farmland and water resources, but rather, socio-economic factors including agricultural technology and irrigation facilities have improved efficiency. The spatial heterogeneity in their driving effects in different major grain producing regions has led grain production to shifted to the northern regions that have few natural resources, posing new challenges for food security: without new policies, China will not be poised to meet increased grain demand projected from 2025 to 2030, especially for rice and soybeans. From a comparison across four grain policies, it is found that region-specific policies will most effectively accelerate transitions to sustainable food production systems. We urge decision-makers to reach beyond natural resource inputs and focus on socio-economic factors, particularly inputs for replanting abandoned farmland and irrigation facilities that can ensure food security in the future.

Decomposition of carbon emissions and companies’ willingness-to-pay for Green Fund: evidence from the manufacturing sector in Busan, South Korea

ABSTRACT The impact of carbon neutrality on the manufacturing sector, through regulatory and carbon pricing mechanisms, pushes companies toward environmental-friendly management. This paper seeks to examine the manufacturing sector’s willingness-to-pay (WTP) for the Busan Green Fund, an initiative designed to incentivize companies to adopt green management practices in Busan Metropolitan City. Three industries with the highest level of CO2 emissions are selected utilizing the Logarithmic Mean Divisia Index (LMDI) method. Companies in these three key industries are surveyed regarding their WTP, beyond their energy costs, for the Busan Green Fund. The study also investigates the characteristics of these companies. The average WTP is an 11.1% premium on energy costs. The regression results show that WTP is significantly influenced by the level of investment in energy-saving technology and company size. Based on our analysis of CO2 emission trends within the manufacturing sector, policy implications are suggested for sustainable development via the operation of a dedicated fund.

Overlooked carbon sinks in the house building industry and the influential factors

The study aims to establish a carbon sink measurement model for the house building industry and analyze the time-varying characteristics of influential factors. China's house building industry exhibited an increasing trend in carbon sink, from 0.336 billion tons in 2000 to 234.1 million tons in 2020. This study defines the carbon sink coefficient of the house building industry to understand its carbon sink capacity. From 2000 to 2020, the average carbon dioxide absorption per square meter of floor space annually, throughout its life cycle, is 2.09 kg. Factors influencing the carbon sink were analyzed using Logarithmic Mean Divisia Index (LMDI) model. The most influential factor was resource consumption intensity, followed by value dependence level and output scale effect. Employing Time-Varying Parameter Vector Autoregression (TVP-VAR), this study examines the impact of macro factors on carbon sinks at various lead times and specific points. Resource consumption intensity exhibits significant time-varying effects. Value dependence level negatively influences, primarily in the medium and long terms, while the output scale effect consistently shows a positive influence, mainly in the long term. Scenario forecasting suggests that by 2035, under low-carbon transition scenarios, carbon sinks could increase by 8.28% and 17.26% compared to baseline and high-carbon crude scenarios, respectively. To foster this growth, prolonging the service life of houses, enhancing construction site management and waste treatment, and employing higher value-added production factors are recommended.

Water peaking with a three‐pillar analysis in China: Exploring the interplay of resource utilization and industrial structure

AbstractThis study introduces the innovative concept of water‐peaking theory, drawing inspiration from the theories of “peak oil” and “carbon peaking.” Employing the Environmental Kuznets Curve, Tapio's decoupling theory, and Logarithmic Mean Divisia Index decomposition, we analyze data spanning from 2006 to 2020, yielding significant findings. First, China's three industries exhibit an imbalanced structure in the utilization of water resources, necessitating the reallocation of water from the primary industry to the secondary and tertiary industries. Second, although China has not yet attained absolute water peaking, it confronts a predicament in sustainable development. Third, the primary and secondary industries demonstrate favorable decoupling of water consumption from economic growth, while the tertiary industry faces challenges in achieving historical water peaking. Fourth, drivers on the production side (technological effects, structural effects, output effects) substantially reduce water consumption across all industries, contributing to progress toward water peaking. Conversely, factors on the consumer side (population effects) play a minor role with untapped potential. This research provides valuable insights for nations worldwide in identifying historical inflection points in water resource management, pursuing sustainable pathways for industrial water usage and economic growth, and achieving environmental quality and sustainable economic development objectives. China's experiences serve as a guiding exemplar in this endeavor.

Analysis of energy consumption and change structure in major economic sectors of Pakistan.

Studying and analyzing energy consumption and structural changes in Pakistan's major economic sectors is crucial for developing targeted strategies to improve energy efficiency, support sustainable economic growth, and enhance energy security. The logarithmic mean Divisia index (LMDI) method is applied to find the factors' effects that change sector-wise energy consumption from 1990 to 2019. The results show that: (1) the change in mixed energy and sectorial income shows a negative influence, while energy intensity (EI) and population have an increasing trend over the study period. (2) The EI effects of the industrial, agriculture and transport sectors are continuously rising, which is lowering the income potential of each sector. (3) The cumulative values for the industrial, agricultural, and transport sectors increased by 57.3, 5.3, and 79.7 during 2019. Finally, predicted outcomes show that until 2035, the industrial, agriculture, and transport incomes would change by -0.97%, 13%, and 65% if the energy situation remained the same. Moreover, this sector effect is the most crucial contributor to increasing or decreasing energy consumption, and the EI effect plays the dominant role in boosting economic output. Renewable energy technologies and indigenous energy sources can be used to conserve energy and sectorial productivity.

Assessing the drivers of energy supply and demand in Sub-Saharan Africa

Ensuring reliable and sustainable energy access in sub-Saharan Africa requires exploring alternatives to traditional biomass and fossil fuels, crucial for overcoming challenges related to limited electricity and clean cooking energy access. Within this background and to assess the factors affecting energy supply and energy consumption, the Logarithmic Mean Divisia Index (LMDI) was applied across 48 sub-Saharan countries and adapted to the regional context considering energy access indicators.Key results showed that economic activity significantly influences both supply and demand growth. However, this growth is restricted by outdated infrastructure, resulting in inefficiencies and increased conversion losses. Policymakers thus face a dual challenge of promoting economic growth while modernizing energy infrastructure to sustain development.Moreover, targeted policies are essential, particularly in energy-intensive in energy-intensive sectors like wholesale and retail, while advocating for a comprehensive approach in the residential sector. This approach involves socioeconomic considerations, energy access enhancement, and the promotion of energy-efficient and renewable technologies to meet rising demand amid population growth and evolving consumption patterns.Furthermore, the study underscores the importance of enhancing both energy access levels and energy efficiency in the residential sector. Unlike the industrial sector, energy intensity is crucial for improving efficiency without compromising the level of access. Enhancing energy efficiency can significantly reduce energy consumption in households, which is important given the high reliance on energy-intensive practices such as cooking with traditional biomass.

A modified approach of the agricultural grey water footprint considering the nitrogen fixation effect of crops in China

The rapidly growing demand for food in human societies has led to the extensive use of fertilizers, significantly contributing to water pollution. Grey water footprints (GWF) serve as a crucial method for measuring Non-point Source (NPS) pollution, particularly in agriculture. Traditional assessments of agricultural GWF neglect biologically fixed nitrogen and the use of organic fertilizers. This research proposed a modified method to assess the GWF of Chinese agriculture from 2000 to 2020, considering the impact of Nitrogen fixation in crops and the use of organic fertilizer. We also analyzed the determinants of Agricultural Nitrogen Fixation Intensity (ANFI) using the Logarithmic Mean Divisia Index (LMDI) method to better understand factors influencing agricultural GWF. Our findings include (1) Grain cereals (e.g., maize, rice, and wheat) significantly contribute to nitrogen fixation in crop organs, accounting for 87.7%, whereas the other six economic crops contribute the rest of 12.3%. Human wastes account for Nitrogen emissions for 1.40%, and emissions by livestock product, red meat contributes 16.26%, while white meat, eggs, and milk collectively contribute 82.34%. (2) Across China, there is an overestimation of GWF by 22.4 hundred million m3 per year, about 5.13% of the total GWF measured by traditional methods. It appears that the overestimation of GWF in plain regions with more arable land tends to be somewhat more pronounced compared to plateau and coastal municipalities. Biotechnological advancements in the capacity of nitrogen fixation for key crops (e.g., maize, wheat, rice) can alleviate agricultural water pollution. The modified methodology provides a robust scientific basis for a more precise application of GWF assessments, highlighting the substantial overestimation by traditional methods in China.

Decoupling state of urban development and carbon emissions and its driving factors and predictions: A case study of Chengdu metropolitan area

Prioritizing the reduction of carbon emissions in key developing urban centers is a significant step toward achieving China's carbon peaking and neutrality goals. We examined the Chengdu metropolitan area as a case study. We proposed a coupled method for inversely estimating nighttime light and carbon emissions from energy consumption, enhancing the accuracy of fitting at the county scale and addressing the issue of missing energy consumption data. We integrated the Tapio decoupling model and the Logarithmic Mean Divisia Index (LMDI) model for comprehensive analysis, mitigating the limitations arising from the use of a singular model. Furthermore, we employed a multi-scenario analysis with an extended STIRPAT model to forecast the carbon emission trajectory of the Chengdu urban agglomeration and elucidate potential pathways for achieving carbon peaking and carbon neutrality goals. The findings indicated the following:(1)carbon emissions are predominantly concentrated in regions characterized by high levels of urban development. Forested land serves as the primary carbon sink; however, its capacity in the Chengdu metropolitan area fails to offset carbon emissions from energy consumption;(2)factor decomposition analysis revealed that economic development was the principal driver of carbon emissions, leading to an increase of 23.7964 million tons. Conversely, energy intensity emerged as the primary mitigation factor, reducing carbon emissions by 11.0677 million tons;(3)The Chengdu metropolitan area is projected to reach a carbon peak by 2030, with the multi-factor coordinated optimization (MFO) model representing the most favorable trajectory, with a projected peak of approximately 15.6998 million tons. However, to achieve carbon neutrality by 2060, there is still a surplus of approximately 10.4808 million tons.

From global to national: The role of urban agglomerations in China's new development paradigm.

Urban agglomerations (UAs), which serve as pivotal hubs for economic and innovative convergence, play a crucial role in enhancing internal circulation and strengthening external linkages. This study utilizes the China city-level multi-regional input-output tables, incorporating the Dagum Gini coefficient and kernel density estimation methods, to perform a thorough quantitative analysis. Disparities within the national and global value chains ("dual value chains") of Chinese UAs from 2012 to 2017 were assessed. Additionally, the logarithmic mean Divisia index (LMDI) method was applied to disaggregate the drivers of both national and global intermediate inputs (NII and GII). The study's key findings include the following: (1) The national value chain (NVC) within UAs exhibits robust growth, contrasting with the decline in the global value chain (GVC). (2) The inter-UA disparity contribution rate significantly surpasses the combined rates of intra-UA contribution and super-variation density. (3) Distinct evolutionary peak trends are discerned among various UAs within the "dual value chains", highlighting diverse spatial polarization characteristics and expansiveness. (4) The growth of the NVC has transitioned from a negative to a positive impact on NII, while the decline in GVC has substantially counteracted GII growth. Economic and demographic factors notably drive positive improvements in both NII and GII, whereas the efficiency of outflows presents a negative driving effect. Based on these findings, this study offers strategic recommendations to facilitate the effective integration of UAs into the new development paradigm, thereby providing a scientific basis for related decision-making processes.

Decomposing Carbon Intensity Trends in China’s Civil Aviation: A Comprehensive Analysis from 1998 to 2019

Carbon emission intensity is an important index reflecting an entity’s low-carbon competitiveness. This paper presents an extended logarithmic mean divisia index (LMDI) model to dissect carbon intensity within China’s civil aviation from 1998 to 2019, revealing a significant reduction in CO2 emissions per air transport revenue. It attributes this decrease to technological advancements, optimized fleet structures, and improved operational efficiencies, highlighting the impact of larger, more efficient aircraft and enhanced load factors. The study also explores economic factors influencing carbon efficiency, suggesting a comprehensive approach encompassing technological innovation and strategic operational improvements for sustainable aviation development.