Search Results (10)

As a designated national low-carbon pilot city, Nanjing faces the challenge of reducing energy consumption and carbon emissions while experiencing rapid economic growth. This study developed a localized Long-range Energy Alternatives Planning System (LEAP) model specifically for Nanjing and constructed four different development scenarios. By utilizing the Log Mean Divisia Index (LMDI) decomposition, the Tapio decoupling elasticity coefficient, and comparing the emission reduction effects of individual measures and their cross-elasticity of carbon reduction, this study investigated the key factors and their carbon reduction path characteristics in Nanjing toward its carbon peak target by 2030. The results indicate that: (i) Nanjing could reach its peak carbon target of about 3.48 million tons by 2025 if carbon reduction measures are strengthened; (ii) The main elements influencing Nanjing’s carbon peak include controlling industrial energy consumption, restructuring the industry, promoting the construction of a new power system, and developing green transportation; (iii) Controlling industrial energy consumption and changing industrial structure have a greater impact on reducing carbon emissions than other measures, and both have a synergistic effect. Therefore, Nanjing should prioritize these two strategies as the most effective methods to reduce carbon emissions. Additionally, to slow down the growth of urban carbon emissions, policies aimed at reducing the energy intensity and carbon intensity of energy consumption should be formulated. For instance, the integration and innovation of green industries within the city region, such as new energy vehicles, new energy materials, and big data, should be accelerated, and the proportion of clean energy consumption in urban areas should be increased. The LEAP (Nanjing) model has successfully explored Nanjing’s low-carbon pathway and provided policy guidance for the optimal transformation of industrial cities and early carbon peaking. Full article

Scientific estimation and dynamic monitoring on the heterogeneity of carbon emission from energy consumption (CEEC) is the basis for formulating and implementing regional carbon reduction strategies to realize the goal of carbon neutrality and high-quality development. This study analyzes the temporal and spatial differences of CEEC and its driving factors in the Yellow River Basin (YRB) from 2000 to 2018 based on the Log-Mean Divisia Index (LMDI) time decomposition method and the multi-regional (M-R) space decomposition method. The results indicate the following: The amount of CEEC of the YRB increased greatly from 2000 to 2012, and then expressed a convergence trend after 2012, with obvious spatial differences. The economic development is the leading factor that promotes the increase in CEEC in the YRB, energy intensity is the main force for the reduction in CEEC, and their influencing effectiveness varies significantly in different periods and provinces. Spatially, the larger economic development in Shandong, Henan, and Sichuan causes the higher level of CEEC, and the regulation of energy intensity in Shanxi, Ningxia, and Inner Mongolia is important for the reduction in their CEEC. The impact effectiveness of economic structure and energy structure on CEEC in the YRB is relatively weak, and they are potential factors for the reduction in CEEC. Therefore, the corresponding emission reduction measures in nine provinces of the YRB should focus on reducing energy intensity, building a green energy system, and strengthening “green” economic development to achieve high-quality development in the YRB. This study is designed to explore the spatiotemporal variations and influencing factors of carbon emissions in the nine provinces of the YRB, which is of great significance for achieving low-carbon development in the region. Full article

Under the targets of peaking CO₂ emissions and carbon neutrality in China, it is a matter of urgency to reduce the CO₂ emissions of road transport. To explore the CO₂ emission reduction potential of road transport, this study proposes eight policy scenarios: the business-as-usual (BAU), clean electricity (CE), fuel economy improvement (FEI), shared autonomous vehicles (SAV), CO₂ emission trading (CET) (with low, medium, and high carbon prices), and comprehensive (CS) scenarios. The road transport CO₂ emissions from 2020 to 2060 in these scenarios are calculated based on the bottom-up method and are evaluated in the Low Emissions Analysis Platform (LEAP). The Log-Mean Divisia Index (LMDI) method is employed to analyze the contribution of each factor to road transport CO₂ emission reduction in each scenario. The results show that CO₂ emissions of road transport will peak at 1419.5 million tonnes in 2033 under the BAU scenario. In contrast, the peaks of road transport CO₂ emissions in the CE, SAV, FEI, CET-LCP, CET-MCP, CET-HCP, and CS scenarios are decreasing and occur progressively earlier. Under the CS scenario with the greatest CO₂ emission reduction potential, CO₂ emissions of road transport will peak at 1200.37 million tonnes in 2023 and decrease to 217.73 million tonnes by 2060. Fuel structure and fuel economy contribute most to the emission reduction in all scenarios. This study provides possible pathways toward low-carbon road transport for the goal of carbon neutrality in China. Full article

Ensuring food security and curbing agricultural carbon emissions are both global policy goals. The evaluation of the relationship between grain production and agricultural carbon emissions is important for carbon emission reduction policymaking. This paper took Heilongjiang province, the largest grain-producing province in China, as a case study, estimated its grain production-induced carbon emissions, and examined the nexus between grain production and agricultural carbon emissions from 2000 to 2018, using decoupling and decomposition analyses. The results of decoupling analysis showed that weak decoupling occurred for half of the study period; however, the decoupling state and coupling state occurred alternately, and there was no definite evolving path from coupling to decoupling. Using the log mean Divisia index (LMDI) method, we decomposed the changes in agricultural carbon emissions into four factors: agricultural economy, agricultural carbon emission intensity, agricultural structure, and agricultural labor force effects. The results showed that the agricultural economic effect was the most significant driving factor for increasing agricultural carbon emissions, while the agricultural carbon emission intensity effect played a key inhibiting role. Further integrating decoupling analysis with decomposition analysis, we found that a low-carbon grain production mode began to take shape in Heilongjiang province after 2008, and the existing environmental policies had strong timeliness and weak persistence, probably due to the lack of long-term incentives for farmers. Finally, we suggested that formulating environmental policy should encourage farmers to adopt environmentally friendly production modes and technologies through taxation, subsidies, and other economic means to achieve low-carbon agricultural goals in China. Full article

Modern agriculture contributes significantly to greenhouse gas emissions, and agriculture has become the second biggest source of carbon emissions in China. In this context, it is necessary for China to study the nexus of agricultural economic growth and carbon emissions. Taking Jilin province as an example, this paper applied the environmental Kuznets curve (EKC) hypothesis and a decoupling analysis to examine the relationship between crop production and agricultural carbon emissions during 2000–2018, and it further provided a decomposition analysis of the changes in agricultural carbon emissions using the log mean Divisia index (LMDI) method. The results were as follows: (1) Based on the results of CO₂ EKC estimation, an N-shaped EKC was found; in particular, the upward trend in agricultural carbon emissions has not changed recently. (2) According to the results of the decoupling analysis, expansive coupling occurred for 9 years, which was followed by weak decoupling for 5 years, and strong decoupling and strong coupling occurred for 2 years each. There was no stable evolutionary path from coupling to decoupling, and this has remained true recently. (3) We used the LMDI method to decompose the driving factors of agricultural carbon emissions into four factors: the agricultural carbon emission intensity effect, structure effect, economic effect, and labor force effect. From a policymaking perspective, we integrated the results of both the EKC and the decoupling analysis and conducted a detailed decomposition analysis, focusing on several key time points. Agricultural economic growth was found to have played a significant role on many occasions in the increase in agricultural carbon emissions, while agricultural carbon emission intensity was important to the decline in agricultural carbon emissions. Specifically, the four factors’ driving direction in the context of agricultural carbon emissions was not stable. We also found that the change in agricultural carbon emissions was affected more by economic policy than by environmental policy. Finally, we put forward policy suggestions for low-carbon agricultural development in Jilin province. Full article

This paper analyzed the annual trends in energy consumption of 14 industries in Korea from 2000 to 2014 using an extended log mean Divisia index (LMDI) method that embedded global value chain (GVC) divisions in the standard LMDI decomposition. Using a world input–output table, we calculated foreign value-added share in the GVC activities for each industry. Based on a Cobb–Douglas production technology, we embedded GVC divisions in the ordinary LMDI factor decomposition. The key findings indicate that the production effect mainly drives energy consumption, while energy consumption has decreased by both the foreign-structure effects and the foreign-intensity effects. Together with a decline in the domestic energy intensity effects, both of the GVC effects have improved energy efficiency. Energy-intensive industries have consumed more energy than other industries, while they have more incentive to save energy costs because these costs are a large proportion of total import costs. The opposite pattern occurred in other industry groups. Industries that do not naturally depend on energy tend to consume more energy and became more energy-intensive. Full article

Agrochemical inputs such as chemical fertilizers and pesticides have been recognized as sources of agricultural non-point source pollution and are controlled in order to prevent further deterioration of water pollution. In consideration of the available and effective measures to improve agricultural output value in a long-term, the key to the adoption of reduction control on agrochemical inputs is to ensure the decoupling relationship of agrochemical inputs to agricultural economic growth and to find out the endogenous growth of agrochemical inputs. This paper analyzed the relationship of agrochemical input consumption and agricultural output value in Eastern China by the Topia decoupling model. Interestingly, the transformation of expansive negative decoupling—expansive coupling—weak decoupling—strong decoupling was exposed, which can be used as a theoretical support to the source reduction control on agricultural non-point source pollution. The source reduction can be influenced of three factors: area factor, agricultural productivity factor and efficiency factor, which were decomposed by applying a log-mean Divisia index (LMDI) method, and the efficiency factor can promote the slowing down of the increase of agrochemical input consumption, while the agricultural productivity factor was the main factor to increase agrochemical input consumption; the area factor was not obvious. In addition to that, the formulation and implementation of source reduction control policies was affected by the differences of the spatial framework in Eastern China, where the source reduction control in different regions would be used to move ahead (or to delay). Full article

China is facing huge pressure on CO₂ emissions reduction. The heavy industry accounts for over 60% of China’s total energy consumption, and thus leads to a large number of energy-related carbon emissions. This paper adopts the Log Mean Divisia Index (LMDI) method based on the extended Kaya identity to explore the influencing factors of CO₂ emissions from China’s heavy industry; we calculate the trend of decoupling by presenting a theoretical framework for decoupling. The results show that labor productivity, energy intensity, and industry scale are the main factors affecting CO₂ emissions in the heavy industry. The improvement of labor productivity is the main cause of the increase in CO₂ emissions, while the decline in energy intensity leads to CO₂ emissions reduction, and the industry scale has different effects in different periods. Results from the decoupling analysis show that efforts made on carbon emission reduction, to a certain extent, achieved the desired outcome but still need to be strengthened. Full article

Decomposition of the urban water footprint can provide insight for water management. In this paper, a new decomposition method based on the log-mean Divisia index model (LMDI) was developed to analyze the driving forces of water footprint changes, attributable to food consumption. Compared to previous studies, this new approach can distinguish between various factors relating to urban and rural residents. The water footprint of food consumption in Xiamen City, from 2001 to 2012, was calculated. Following this, the driving forces of water footprint change were broken down into considerations of the population, the structure of food consumption, the level of food consumption, water intensity, and the population rate. Research shows that between 2001 and 2012, the water footprint of food consumption in Xiamen increased by 675.53 Mm³, with a growth rate of 88.69%. Population effects were the leading contributors to this change, accounting for 87.97% of the total growth. The food consumption structure also had a considerable effect on this increase. Here, the urban area represented 94.96% of the water footprint increase, driven by the effect of the food consumption structure. Water intensity and the urban/rural population rate had a weak positive cumulative effect. The effects of the urban/rural population rate on the water footprint change in urban and rural areas, however, were individually significant. The level of food consumption was the only negative factor. In terms of food categories, meat and grain had the greatest effects during the study period. Controlling the urban population, promoting a healthy and less water-intensive diet, reducing food waste, and improving agriculture efficiency, are all elements of an effective approach for mitigating the growth of the water footprint. Full article

With China’s rapid economic growth, energy-related CO₂ emissions have experienced a dramatic increase. Quantification of energy-related CO₂ emissions that occur in China is of serious concern for the policy makers to make efficient environmental policies without damaging the economic growth. Examining 33 productive sectors in China, this paper combined the extended “Kaya identity” and “IPAT model” with the Log-Mean Divisia Index Method (LMDI) to analyze the contribution of various factors driving of energy-related CO₂ emissions in China during 1995–2009. Empirical results show that the main obstacle that hinders China’s transition to a green energy economy is the economic structure characterized by high carbon emissions. In contrast, the increased proportion of renewable energy sources (RES) and the improvement of energy efficiency play a more important role in reducing carbon emissions. Moreover, the power sector has a pivotal position in CO₂ emissions reduction, primarily because of the expansion of electricity consumption. These findings suggest that policies and measures should be considered for various industrial sectors to maximize the energy efficiency potential. In addition, optimizing the industrial structure is more urgent than adjusting the energy structure for China. Full article