Exploring Reduction Potential of Carbon Intensity Based on Back Propagation Neural Network and Scenario Analysis: A Case of Beijing, China

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

The international community is facing the challenge of climate change, and the rapid increase of energy consumption plays a central role in the increasing carbon dioxide emissions. With 31 provinces, municipalities and autonomous regions in mainland China, this article establishes an impact model of carbon intensity, identifies key influencing factors on carbon intensity, and quantifies the effect of primary energy consumption, coal share, economic growth and technological innovation on carbon intensity in Eastern, Central and Western China. Results show that 1% of the increase in primary energy consumption in these three areas is attributable to a 0.738%, 0.975% and 0.742% increase in carbon intensity; 1% of the decrease in coal share is attributable to a 0.346%, 0.604% and 0.508% decrease in carbon intensity; and 1% of the increase in productivity efficiency should induce a 0.926%, 1.042% and 0.851% decline in carbon intensity, respectively. The results suggest that it is critical to integrate means for energy conservation, technological and productivity efficiency innovation, and adjust energy structure measures to fully implement a carbon intensity reduction strategy for China in the future.

Objective (25-75 words) Energy incumbents face risks and opportunities from the energy transition, requiring portfolio-wide decarbonisation (typically >30-40% CI (Carbon Intensity) reduction by 2030) necessitating structural change. Traditional merger & acquisition evaluation lacks systematic integration of carbon intensity as a core performance metric. This paper introduces a practical framework to manage portfolio transformation as a key lever for Carbon Intensity reduction, demonstrated through a composite case study for an Oil & Gas Major targeting ∼30% portfolio CO2e abatement while maximizing commercial value. Method (75 – 100 Words) Achieving step-change Carbon Intensity reduction via acquisitions and divestments (A&D) requires moving beyond purely financial metrics. Distilling "best in class" project experience and lessons, we present a structured, stage-gated framework embedding Carbon Intensity management throughout the portfolio transformation lifecycle by: Recognising risk though Carbon Intensity targets and baselines to quantify portfolio risk exposure and defining Carbon Intensity-weighted A&D screening criteria. Building value by mandating Carbon Intensity verification and impact modelling within due diligence/valuation, linking Carbon Intensity to near-term efficiency and long-term value drivers (e.g., access to capital). Enabling the vision by implementing governed execution and portfolio-level Carbon Intensity tracking to ensure long-term strategic transformation. Results (100 – 200 Words) Risk Management Foundation: The framework first addresses Carbon Intensity as a strategic risk. Establishing a clear target Carbon Intensity state and mapping the baseline identifies high-Carbon Intensity risk assets. Carbon Intensity-weighted screening criteria explicitly filter targets based on their risk/contribution profile before significant portfolio activity. Integrating Carbon Intensity for Commercial Value: Rigorous due diligence quantifies the Carbon Intensity impact of potential opportunities and associated carbon costs/risks. The valuation explicitly incorporates Carbon Intensity factors and carbon pricing (e.g. AUD$50−70/tCO2e), demonstrating how lower Carbon Intensity contributes directly to commercial attractiveness (∼AUD$86MM portfolio net present value, NPV, in the case study). This links decarbonisation to near-term financial evaluation and positions the portfolio for long-term access to debt/capital markets. Governed Execution for Long-Term Vision: The framework aligns Carbon Intensity-focused portfolio transformation with corporate investment governance, requiring proposals to articulate Carbon Intensity reduction contributions alongside financial justification. Approved transactions are sequenced onto a transformation roadmap, enabling board-level stewardship beyond short executive tenures. AI-driven portfolio management systems provide ongoing visibility and action nudging, tracking actual Carbon Intensity reduction against the target pathway and verifying the realised financial value, ensuring strategic goals translate into sustained results – a long-term perspective inherent to successful major energy companies. Novelty (25 – 75 words) This paper presents a practical framework for proactively managing portfolio carbon intensity transformation. By embedding Carbon Intensity as a core risk and value driver within screening, due diligence, valuation, and long-term governance – demonstrated through the composite case study achieving a ∼30% abatement pathway with positive NPV – operators can identify and execute the most commercially attractive transactions to meet significant targets, effectively transforming their asset base for sustained value creation in a lower-carbon future.

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