Studying regional low-carbon development: A case study of Sichuan Province in China.

Summary Cities, contributing more than 75% of global carbon emissions, are at the heart of climate change mitigation. Given cities' heterogeneity, they need specific low-carbon roadmaps instead of one-size-fits-all approaches. Here, we present the most detailed and up-to-date accounts of CO2 emissions for 294 cities in China and examine the extent to which their economic growth was decoupled from emissions. Results show that from 2005 to 2015, only 11% of cities exhibited strong decoupling, whereas 65.6% showed weak decoupling, and 23.4% showed no decoupling. We attribute the economic-emission decoupling in cities to several socioeconomic factors (i.e., structure and size of the economy, emission intensity, and population size) and find that the decline in emission intensity via improvement in production and carbon efficiency (e.g., decarbonizing the energy mix via building a renewable energy system) is the most important one. The experience and status quo of carbon emissions and emission-GDP (gross domestic product) decoupling in Chinese cities may have implications for other developing economies to design low-carbon development pathways.

Effectively addressing today’s energy challenges requires advanced technologies along with policies that influence economic markets while advancing the public good.

In today's highly advanced industrialised and modernised world, China's economy is still growing, and its demand for energy is increasing daily. It is crucial to examine the connection between energy consumption, carbon emissions, and economic growth in order to promote economic growth based on energy conservation and emission reduction. Using Dezhou City in Shandong Province as an example, the study builds a VAR model of carbon emission, energy consumption, and economic growth in Dezhou City based on simplified macroeconomic sub-models, energy sub-models, and environmental sub-models. It then determines the correlation and influence mechanism between the three using tests like ADF unit root and Granger causality. The pertinent elements affecting Dezhou's carbon emissions were then investigated using grey correlation analysis. Finally, based on the study's findings, policy suggestions are made regarding energy use, carbon emissions, and economic expansion. It is necessary not only to restrain high-energy consumption industries and fundamentally optimize the energy consumption structure, but also to find new economic growth points and improve economic growth channels, so as to optimize the industrial structure. In this process, increasing the proportion of the tertiary industry is a key measure. In addition, the government needs to advocate the citizens to adopt a low-carbon lifestyle, and the concept of low-carbon environmental protection will be deeply rooted in the hearts of the people. This study will provide suggestions and theoretical guidance for China's energy consumption and carbon emissions, and help achieve high-quality growth of China and even the world economy.

The development process that uses excessive fossil energy in industrial activities impacts increasing carbon emissions in Indonesia. The purpose of this study is to determine how the relationship between energy consumption (EN), economic growth (GDP), carbon emissions (CO2), and human development (HDI) in Indonesia in the Maqashid Syariah review. Using the Vector Error Correction Model (VECM) approach, it is found that in the long run, carbon emissions negatively affect energy consumption. Meanwhile, economic growth and human development positively affect energy consumption. Energy consumption and economic growth positively affect human development in the short term. Meanwhile, carbon emissions hurt HDI, while HDI has a positive effect on carbon emissions. Meanwhile, the Granger causality test results show that HDI has a unidirectional causality relationship with Indonesia's energy consumption, economic growth, and carbon emissions. This result indicates that energy consumption in Indonesia has not provided protection and benefits for the community by Maqashid Sharia, which prioritizes the principle of sustainable development

Analysis of energy consumption and carbon emission during the urbanization of Shandong Province, China

The establishment of low-carbon cities has been suggested all over the World, since cities are key drivers of energy usage and the associated carbon emissions. This paper presents a scenario analysis of future energy consumption and carbon emissions for the city of Beijing. The Long-range Energy Alternatives Planning (LEAP) model is used to simulate a range of pathways and to analyze how these would change energy consumption and carbon emissions from 2007 to 2030. Three scenarios have been designed to describe future energy strategies in relation to the development of Beijing city, namely a reference scenario (RS), control scenario (CS), and integrated scenario (IS). The results show that under the IS the total energy demand in Beijing is expected to reach 88.61 million tonnes coal equivalent (Mtce) by 2030 (59.32 Mtce in 2007), 55.82% and 32.72% lower than the values under the RS and the CS, respectively. The total carbon emissions in 2030 under the IS, although higher than the 2007 level, will be 62.22% and 40.27% lower than under the RS and the CS, respectively, with emissions peaking in 2026 and declining afterwards. In terms of the potential for reduction of energy consumption and carbon emissions, the industrial sector will continue to act as the largest contributor under the IS and CS compared with the RS, while the building and transport sectors are identified as promising fields for achieving effective control of energy consumption and carbon emissions over the next two decades. The calculation results show that an integrated package of measures is the most effective in terms of energy savings and carbon emissions mitigation, although it also faces the largest challenge to achieve the related targets.

PDF HTML阅读 XML下载 导出引用 引用提醒 基于LMDI分解的厦门市碳排放强度影响因素分析 DOI: 10.5846/stxb201304020585 作者: 作者单位: 中国科学院城市环境与健康重点实验室,中国科学院城市环境研究所,水利部珠江水利委员会,中国科学院城市环境与健康重点实验室,中国科学院城市环境研究所,中国科学院研究生院;中国科学院城市环境与健康重点实验室,中国科学院城市环境研究所,赤峰学院，资源与环境科学学院 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目（71003090和71273252）；福建省自然科学基金资助项目（2012J01306） Factor decomposition of carbon intensity in Xiamen City based on LMDI method Author: Affiliation: Institute of Urban Environment, Chinese Academy of Sciences,,Institute of Urban Environment, Chinese Academy of Sciences,, Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:研究碳排放强度的变化趋势及其影响因素对于指导低碳城市建设具有重要意义。应用对数平均权重分解法（LMDI），基于厦门市2005-2010年各部门终端消费数据对碳排放强度指标进行因素分解，并将传统分析仅注重产业部门的能源碳排放，拓展到全面考虑产业部门和家庭消费的能源活动和非能源活动影响。研究结果表明：2005-2010年厦门市碳排放强度下降17.29%，其中产业部门能源强度对总碳排放强度变化影响最大（贡献63.07%），家庭消费能源强度是碳排放强度下降的主要抑制因素（-45.46%）。从影响效应角度看，经济效率对碳排放强度下降贡献最大，碳排系数减排贡献最小；从部门减排贡献角度看，第二产业贡献最大，家庭消费贡献最小。总体而言，厦门市未来碳减排重点部门在第二产业，优化产业结构和能源结构有较大减排潜力。 Abstract:It is of great significance for guiding the low-carbon city development to explore the trends and influencing factors of carbon intensity. Most traditional decomposition studies only focused on the energy carbon emissions from industrial sectors. This paper extended the application of the Logarithmic Mean weight Divisia Index (LMDI) method to a full consideration of the industrial and household sectors, as well as their energy and non-energy activities. Taking Xiamen City as a study case, the carbon emissions was calculated by IPCC's methods based on the end-use consumption data of the industrial and household sectors from 2005 to 2010. Then the aggregated carbon intensity was decomposed by LMDI method into ten driving factors, which covering energy and non-energy related emissions from industrial and household sectors. The ten driving factors were further categorized into four groups: carbon emission efficiency effect (including efficiency factors of energy related industrial carbon emissions, energy related household carbon emission, non-energy related industrial carbon intensity, and non-energy related household carbon intensity), energy intensity effect (including industrial energy intensity factor and that of household), industry structure effect (energy related industrial structure factor and non-energy one) and economic efficiency effect (energy related economic efficiency factor and non-energy one). Results showed that carbon intensity of Xiamen City decreased by 17.29% from 2005 to 2010. From perspective of driving factors, the energy intensity of industrial sector had the greatest effect on carbon intensity reduction (a contribution rate of 63.07%), and the energy intensity of household sector was the largest hinder of carbon intensity reduction (-45.46%). So energy intensity had significant impact on carbon intensity reduction for Xiamen City. Except for reducing the energy intensity of industrial sectors, it is also very important to control the growth of household's energy intensity at the same time. From the effect perspective, the economic efficiency effect became the dominant driver of carbon intensity reduction, followed by energy intensity effect and industry structure effect, and carbon emission efficiency effect contributed the less. The economic efficiency contributed 50.85% of total carbon intensity reduction, which greatly promoted household's carbon intensity reduction. Although industrial structure adjustment had relatively small effects at the study periods, the industry structure in which secondary industry has large proportion is anticipated to have large reduction potentials in the future. The carbon emission efficiency effect was chiefly determined by energy structure, and the current carbon-intensive energy structure also has large reduction potentials. From the sector perspective, the contribution of the secondary industry was the largest (contributing 67.04%), sequentially followed by the primary industry, the tertiary industry, and the household sector. The carbon intensity reduction by secondary and tertiary industries mainly lied in energy related carbon emissions; whereas the carbon intensity reduction by the primary industry and household sectors mainly relied on non-energy emissions. Thus the non-energy related carbon emissions were an non-negligible part while analyzing carbon intensity reduction. Even though energy efficiency of household sector was the biggest disincentive to reduce carbon intensity, household sector had the less contribution on carbon intensity reduction due to other factors' offset effect. Furthermore, the key sector for future carbon reduction lies on the secondary industry. However, the primary Industry and household sector has limited reduction potential. Overall, optimizing industry structure and energy structure have large reduction potential, and secondary industry has largest reduction potentials. 参考文献 相似文献 引证文献

With rapid economic development and energy consumption growth, China has become the largest energy consumer in the world. Impelled by extensive international concern, there is an urgent need to analyze the character- istics of energy consumption and related carbon emission, with the objective of saving energy, reducing carbon emission, and lessening environmental impact. Focusing on urban ecosystems, the biggest energy consumer, a method for estimating energy consumption and related carbon emission was established at the urban sector scale in this paper. Based on data for 1996-2010, the proposed method was applied to Beijing in a case study to analyze the consumption of different energy resources (i.e., coal, oil, gas, and electricity) and related carbon emission in different sectors (i.e., agriculture, industry, construction, transportation, household, and service sectors). The results showed that coal and oil contributed most to energy consumption and carbon emission among different energy resources during the study period, while the industrial sector consumed the most energy and emitted the most carbon among different sectors. Suggestions were put forward for energy conservation and emission reduction in Beijing. The analysis of energy consumption and related carbon emission at the sector scale is helpful for practical energy saving and emission reduction in urban ecosystems.

This article focuses on the component production stage of industrialized buildings. The factors affecting energy consumption and carbon emission during the production of fabricated composite plates in a factory in Jiangsu province are classified, the calculation formula is arranged, and the proportion of total energy consumption and carbon emission is analyzed. Based on the measured data, the distribution of energy consumption and carbon emission during the component production stage of fabricated composite plate is summarized.

After more than two decades of negotiation, the China–Russia gas deal represents a new era of energy cooperation between China and Russia. In total, this is a win–win deal for both sides. For China, the deal will decrease energy consumption and carbon emission but will not significantly influence air quality; for Russia, it will provide a new market for its gas resources. In this study, we calculated the energy consumption, carbon emission, and particulate matter pollution (PM2.5 and PM10) in China in 2020, 2030, 2040, and 2050 under four IPCC representative concentration pathways (RCPs 8.5, 6.0, 4.5, and 2.6). We found that energy consumption and carbon emission decreased under the gas deal in RCPs 8.5, 6.0, and 4.5, although the rate of decrease slowed over time; however, in RCP 2.6, the rate of decrease of energy consumption and emission increased over time. PM2.5 and PM10 emission showed similar trends but with increasing rate, although the gas deal would mitigate air pollution in the short term. Although China’s government hopes to reduce carbon and pollutant emission under the deal, our results suggest that additional mitigation measures will be necessary to achieve this goal. Nonetheless, the reduction in carbon emission suggests that the China–Russia gas deal provides a model that other countries can follow to slow climate change.

Analyzing the impacts of technological progress on agricultural energy consumption and carbon emissions is of great significance for the development of low-carbon agriculture. Most of the existing studies focus on the agricultural sector level and lack of assessment of the impacts of technological progress on agricultural energy use and carbon emissions from the perspective of crops. In this study, we evaluated the impacts of technological progress on the energy consumption and carbon emissions of main crops in China under energy intensity constraints using a price endogenous partial equilibrium model with scenario analysis. We found that China's agriculture will have the highest yield and social welfare in 2025 under the production technological progress scenario, which will be 695.44 million t and 287.91 million yuan. Energy consumption for production will be the least under the energy technology progress scenario, which will be reduced by 9.02 million t ce or 16.01% compared to the baseline scenario. Under energy intensity constraints, synergy progress in production and energy technology will be the most effective way to reduce carbon emissions in the agricultural sector. Compared to the baseline, China's agricultural sector will reduce carbon emissions by 22.18 million t c in 2025 under the synergy scenario, a decrease of 16.18%. Therefore, we suggested that China's agricultural sector should pay more attention to the synergetic development of agricultural energy and production technology to further reduce carbon emissions and promote the development of green agriculture.

Influencing factors of carbon emissions in transportation industry based on C[sbnd]D function and LMDI decomposition model: China as an example

This study explores the integration of Building Information Modeling (BIM) technology with green building practices against the backdrop of global building energy consumption and carbon emissions, accounting for significant proportions of total energy use and emissions. The research highlights the importance of BIM in achieving low-carbon and sustainable building development, which is crucial for environmental protection and meeting carbon neutrality goals. This paper examines BIM's applications in daylight analysis, HVAC load calculation, and energy consumption analysis within the building lifecycle. It demonstrates how BIM, combined with energy simulation software like EnergyPlus and IES VE, optimizes building design, construction, and operation for enhanced energy efficiency and reduced carbon emissions. The study reveals that BIM enables precise daylight simulation, optimizing building orientation and design to increase natural lighting and reduce artificial lighting use. In HVAC load calculations, BIM provides detailed building data for accurate load assessments, leading to efficient system designs. Furthermore, BIM's integration with energy analysis tools allows for comprehensive energy consumption prediction and optimization throughout a building's lifecycle, from construction to operation, improving energy management and reducing operational costs. This research underscores the potential of BIM technology to drive innovation in green building practices, offering valuable insights for the construction industry's transition toward sustainability and carbon reduction.

Hainan is the first province in China to announce a comprehensive ban on the sale of fuel vehicles by 2030, aimed at promoting low-carbon transportation development. This paper uses the top-down method of the IPCC to estimate the transportation carbon emissions in Hainan Province from 2006 to 2020. At the same time, considering the impact of structural adjustments, technological advancements, and urbanization developments, the traditional LMDI model was improved and combined with the Tapio model to analyze the decoupling status of transportation carbon emissions and economic development in Hainan Province, as well as the contribution values and rates of various influencing factors. The study reveals: 1) The total transportation carbon emissions increased by 208.68 ten thousand tons, showing a trend of rapid initial rise followed by gradual slowdown, with oil energy accounting for 99% of emissions, of which kerosene contributed the highest proportion and exhibited the greatest increase; 2) The decoupling index displayed an “M” curve fluctuation, indicating an initial deterioration followed by improvement, reflecting an unstable decoupling status; 3) The introduction of urbanization effects showed that positive drivers include income urbanization, spatial urbanization, transportation intensity, and industrial structure effects, while negative drivers encompass energy intensity, population urbanization, and energy structure effects. Among these, the paramount factor driving the increase in carbon emissions is income urbanization effect, whereas the paramount factor curbing carbon emissions is energy intensity effect. Finally, policy recommendations are proposed for carbon emission reduction in the transportation sector of Hainan Province.

Excessive carbon emissions will cause the greenhouse effect and global warming, which is not conducive to environmental protection and sustainable development. In order to realize the goal of “carbon peak and carbon neutrality” as soon as possible, this paper utilizes the methodology provided by the IPCC to measure the carbon emissions and carbon intensity of China’s energy consumption. The classification method of carbon emission and the kernel density function method are used to explore the spatial and temporal evolution of regional carbon emissions. Based on the Log Mean Divided Index (LMDI) method, the drivers of China’s energy carbon emissions are measured. Based on the Tapio index function and the catch-up decoupling model, the decoupling status of Chinese provinces and the development gap with the benchmark provinces are examined. The results show that (1) China’s total energy carbon emissions show a “rising-declining-rising” trend from 2005 to 2021, and reach the first peak in 2013, totaling 1,484,984.406 million metric tons. China’s Hebei, Shanxi, and Shandong provinces have the highest energy carbon emissions. (2) China’s energy carbon emissions are influenced by multiple factors, and the contribution of each factor to energy carbon emissions is in the following order: economic development effect > energy intensity effect > energy structure effect > population size effect. (3) China’s catch-up provinces develop their economies at the expense of the environment and energy consumption.