中国工业领域能源生产力评价.pdf

 中国工业领域能源生产力评价 Construction and case analysis of energy productivity evaluation system in industry field  上海交通大学 Shanghai Jiao Tong University 2017年6月20日   项目信息 项目资助号:G-1509-23752 Grant Number: G-1509-23752 项目期: 09/01/2015 - 08/31/2016 Grant period: 09/01/2015 - 08/31/2016 所属领域:工业 Sector: Industry 项目概述: 针对中国工业能耗在国民经济总能耗占比大的特点，项目选择我国工业领域开展能源生产力指标体系研究。项目通过建立中国工业领域能源生产力评价体系，选择上海市作为城市代表进行工业领域能源生产力评价，并确定上海市工业领域2030能源生产力目标。以此为基础探索如何优化能源消费方式、提高能源生产力，促进经济的持续增长和能源可持续发展，形成上海市中长期能源生产力目标及实施途径政策建议。项目会选择典型工业园区进行案例分析以进一步在政府、企业、公众之中推广能源生产力这一概念。项目将对能源生产力评价在我国的推广、应用起到重要的促进作用，通过能源生产力这一评价体系的广泛应用全面综合的评价我国工业领域的能源使用状况，真正实现能源消耗降低、经济发展、环境排放降低、社会福利提高等多方的多赢结果。项目的实施不仅对我国工业领域能源生产力评价体系的应用以及节能减排将起到示范作用，从而促进工业领域提高能源生产力；同时将对能源生产力这一评价指标体系在全国乃至各地区、各个行业的应用起到重要引领作用。 Project Discription: The assessment index and methodology of energy productivity in China's industrial sectorwill be established in this project. Medium and long-term energy productivity targets and the implementation of the policy recommendations for Shanghai will be formed by using the assessment index and methodology. On the basis of this, it explores how to optimize energy consumption patterns, increase energy productivity, promote sustainable economic growth and energy sustainable development. Case analysis of the typical industrial park, it can further promote the extention the concept of energy productivity in the government, enterprises and the public. Project execution play an important promoting role on the popularization and application of energy productivity assessment in China. China's industrial sector energy use status can be evaluated comprehensively be the energy productivity assessment system build in this project. And also truly realize the reduction of energy consumptionand environmental emissions, improving the economic development and social welfare, and harvest ther multi-party win-win results. Project implementation of the energy productivity evaluation in the popularization and application of our country to an important role in promoting, through energy productivity in the evaluation system of the widely used comprehensive evaluation of China's industrial sector energy use situation, the real energy consumption decreased, reduce the economic development, environmental emissions, improving social welfare and   other multi-party win-win results. The implementation of the project will not only have a demonstration effect on the assessment system of energy productivity in the industrial field, but also can promote the industry to improve the energy productivity. At the same time, the assessment index system of energy productivity will play an important leading role in the industry sector and even the whole country. 项目成员：黄震、谢晓敏、于立军、张庭婷、姚丽珍、李加良、钟蓉、王黎明、郝存、夏淳 Project team: Zhen Huang, Xiaomin Xie, Lijun Yu, Tingting Zhang, Lizhen Yao, Jialiang Li, Rong Zhong, Liming Wang, Cun Hao, Chun Xia 关键词: 能源生产力，工业领域，评价体系 Key Word: Energy productivity, Industry field, Evaluation system 本报告由能源基金会资助。 报告内容不代表能源基金会观点。 This report is funded by Energy Foundation. It does not represent the views of Energy Foundation. i  摘 要 节能减排，实现低碳经济发展，已经成为当前国际社会应对气候变化所关注的焦点。2015年中国政府提出了明确的节能减排目标，并承诺到2030年使单位国内生产总值二氧化碳排放比2005年下降60%65%。要实现已制定的节能减排目标，需要在科学分析中国经济发展、能源消费与排放的特征及其区域差异的基础上，制定科学的区域节能减排发展战略。工业在产业结构中虽然占据的比例不是最高，但其能源消耗占比却是最高的。囿于传统能源强度、能源效率等指标在评价行业发展的局限性，本研究以工业领域为研究对象，提出用能源生产力来衡量工业经济发展，旨在最大化工业产出的同时，还能减少投入、降低排放。 为此，本研究基于数据包络分析（DEA）和随机前沿（SFA）的方法，建立了基于全要素理论的能源生产力评价方法，并用该方法分别对“十五”、“十一五”和“十二五”期间中国各省区工业领域以及上海市各类工业行业的能源生产力变动及其影响因素进行了研究。所考虑的投入要素包括资本、劳动力、能源，产出要素包括期望产出工业增加值以及非期望产出CO2。在对比不同方法的基础上，本报告选用合适的方法对上海市某一工业园区的能源生产力进行了典型性分析。最后，报告还预测了2030年上海市工业能源生产力，并预测了达到该能源生产力下上海市的工业领域CO2排放量。 DEA方法下得到的有关结果如下： 首先，规模效益不变（CRS）假设下的工业生产效率小于或等于规模效应变化（VRS）假设下的工业生产效率；非期望产出CO2作为投入处理时的工业生产效率小于或等于作为副产出处理时的工业生产效率。 其次，对中国各省市工业能源生产力的实证结果表明：中国工业领域能源生产力的提高受技术进步的驱动，当CO2作为投入时，碳排放-能源比对能源生产力的提高起抑制作用；从地区分布来看，中国东部沿海地区工业能源生产力相对较高，西部地区则相对较低，并且在“十二五”期间这种差距有拉大的趋势，反映出中国地区发展的不均衡性；从影响因素分解结果可知，资本投入对中国能源生产力的提高也有重要的促进作用；能源投入在“十一五”期间对所有经济区域工业能源生产力提高表现为ii  抑制作用，但到“十二五”期间这种抑制作用减小，并且部分地区表现为促进作用。 再者，从上海市各类工业行业的能源生产力变动结果可知：技术进步和资本投入对上海能源生产力的提高都起到了促进作用，当CO2作为投入处理时，碳排放-能源比在一定程度上限制了能源生产力的提高；劳动力投入和产出结构对上海市工业能源生产力的影响较小；能源投入对上海工业中的制造业从2009年以来起到重要的促进作用，但对电力、热力、燃气及水生产和供应业起抑制作用。此外，从能源生产力水平方面来看，工业产业附加值越高，其能源生产力也越高。能源密集型工业产业能源生产力提高的主要阻力来源于能源结投入和碳排放。 SFA得到的主要结果如下： 首先，采用参数法中的随机前沿分析法对我国各省市工业领域的能源生产力进行了实证研究。结果表明，资本和劳动力是我国工业能源生产力的驱动性因素，而能耗和碳排放则是抑制因素；在“十五”到“十二五”三个时期里，我国工业领域的平均生产技术效率水平较高，实际产出接近最佳前沿面产出，其中，平均技术效率水平最高的是上海，其次为北京。 其次，基于随机前沿分析法对上海市工业领域33个子行业进行了能源生产力实证分析。结果表明，资本是上海市工业能源生产力增长的主要驱动因素，而能耗和碳排放是抑制因素；相对于生产前沿面来说，上海市工业领域的经济产出还有19%的提升空间；电力、热力、燃气及水生产和供应业的技术无效率程度极高，生产点严重滞后于最优产出。 DEA和SFA两种方法的对比结果表明： 两种不同的方法带来的结果不尽相同，DEA在处理面板数据时，表现出了该法的缺陷和不足。相对于SFA，DEA在处理不同行业时，有很大的局限性。SFA较为合适，可用作对能源生产力进行分解。 2030年上海市工业能源生产力和碳排放 基于索洛余值总量生产函数，采用回归预测法，预测了2020年上海市工业领域的能源生产力及碳排放情况。预测结果显示，按照当前的发展情形，到2020年，上海市工业领域的能源生产力将达到1.09，相比于2013年增长了37%；碳排放强度将为0.97万吨/亿元，相比于2013年下降13.4%。 2030年上海市工业可为国家实现二氧化碳减排承诺作出贡献。情景一中2030年iii  上海市工业领域的能源生产力为1.47亿元/吨标煤，相比2013年提高了83.8%，碳排放为8063.31万吨，相比2013年增加了42.6%，单位生产总值碳排放为0.82万吨/亿元，相比2013年减少了26.8%；情景二中，2030年上海市工业领域的能源生产力为1.21亿元/吨标煤，相比2013年提高了51.3%，碳排放为3619.59万吨，相比2013年减少了36.0%，单位生产总值碳排放为0.53万吨/亿元，相比2013年减少了52.6%。 iv  Abstract To achieve low carbon economic development, energy saving and emission reduction has become the focus of the current international community to deal with climate change. In 2015, the Chinese government put forward a clear goal of energy conservation and emission reduction, and promised to make the carbon dioxide emissions per GDP decreased by 60%65% by 2030 compared with that of 2005. To achieve the goal, a scientific energy conservation and emission reduction development strategy should be developed based on the scientific analysis of China's economic development. Although the proportion of industry in the industrial structure is not the highest, but its energy consumption accounts the highest of the total energy consumption. Due to the limitations of traditional evaluation index such as energy intensity, energy efficiency and others, the index of energy productivity (EP) was proposed in this study to measure the development of industrial economy, for the purpose of maximizing industrial output, reducing input, and reducing emissions. Therefore, this study established an evaluation system of total factor energy productivity by using envelopment analysis (DEA) and stochastic frontier analysis (SFA) method. Empirical analysis were conducted with this method to evaluate the EP and related influence factors of industry in China and Shanghai during “10th Five-Year”, “11th Five-Year”, and “12th Five-Year”. In this model, the input factors considered include capital, labor, energy, while the output factors include the expected output value like industrial added value and the unexpected output of CO2. Based on the comparions of different method, a typical analysis was conducted on an industrial park in Shanghai as well. Besides, the energy productivity and carbon emission for Shanghais industry in 2030 were also predicted.  Results from DEA analysis The results of the energy productivity evaluation model showed that the energy production efficiency under assumption of constant return to scale (CRS) is less than or equal than that of variable return to scale; the industrial production efficiency under the condition of treating undesirable output CO2 as input is less than or equal than that of treating it as byproduct. v  The empirical analysis on Chinas provincial industrial energy productivity showed that technical change was the main driving force to the EP improvement, while the ratio of carbon emission to energy input inhited the EP improvement when CO2 was treated as input. From the point of regional distribution, industrial EP in eastern coastal area of China was relatively high, while in the western region, EP was relatively low. Meanwhile, the gap is widening during the "12th Five-Year" period, which reflecting the imbalance of regional development China. The influence factor decomposition results showed that the capital input has important promoting role to the EP, energy input showed inhibitory effect to all the regional industrial EP in the "11th Five-Year" period, but in the "12th Five-Year" period, it showed promoting effects for some region. The Shanghais EP results showed that technical change, and capital input have positive role to the EP change, while the ratio of carbon esmission to energy input has negative role to the EP change. Labor input and output structure have little effect on Shanghai's industrial energy productivity. Energy structure was positive to Shanghais manufacturing industry since 2009, but inhibit to electricity, heat, gas and water production and supply industry. In addition, from the perspective of EP levels, the higher the industrial added value, the higher its energy productivity. The main resistances to EP increase of energy intensive industries were attributed to energy structure, and carbon emissions. Results from SFA analysis Firstly, the stochastic frontier analysis method is used to study the industrial energy productivity of provinces and cities in China. The results show that capital and labor force are the driving factors of industrial energy productivity in China, while energy consumption and carbon emission are the inhibitory factors. During the three period, 10th Five-Year, 11th Five-Year and 12th Five-Year, the average level of China's industrial technical efficiency is high, the actual output is close to the frontier output. Whats more, Shanghai has the highest average level of technical efficiency, followed by Beijing. Secondly, based on the stochastic frontier analysis, this paper analyzes the energy productivity of 33 sub-sectors in Shanghai's industrial sector. The results show that capital is the main driving force of industrial energy productivity in Shanghai, while energy consumption and carbon emission are the inhibiting factors. Compared with the frontier of production, there are still 19% improvement in space for Shanghais industrial economic vi  output. Electricity, heat, gas and water production and supply industries have a high degree of technical inefficiency, and their  productive points seriously lag behind the optimal output. The comparison of results between DEA and SFA method The results fo the two methods are different. DEA method shows the defects and shortcomings when dealing with the panel data. Compared SFA, DEA has great limitations in dealing with different industries. SFA is appropriate and can be used to decompose energy productivity in this study. Energy productivity and carbon emission for Shanghai industry in 2030 Based on the Solow residual value production function, the energy productivity and carbon emission of Shanghai industry in 2020 are forecasted by regression method. The forecast results show that, according to the current development, Shanghai's industrial energy productivity will reach 1.09 in 2020, a 37% increase compared to 2013. By 2020, carbon intensity will be 0.97 10000t per 0.1billion yuan, down 13.4% compared to 2013.  In 2030, Shanghai's industry could contribute to the country's commitment to achieve carbon dioxide reduction. The results in Scenario 1 showed that the energy productivity and carbon emission were 83.8% and 42.6% higher than that in 2013, while carbon emission per GDP were 26.8% lower. In Scenario 2, the energy productivity was 51.3% higher than that in 2013, while the carbon emission and carbon emission per GDP were 36.0% and 52.6% lower than that in 2013, respectively.  I  目 录 摘 要 . i Abstract . iv 目 录 . I 主要缩略词说明 . V 第一章 绪论 . 1 1.1 引言 . 1 1.1.1 中国工业领域取得的成就 . 1 1.1.2 中国工业发展带来的环境问题 . 2 1.1.3 经济新常态下的工业发展目标 . 3 1.2 中国工业能源消耗与污染物排放 . 4 1.2.1 中国主要能源消耗现状 . 4 1.2.2 工业能源消耗 . 7 1.2.3 工业污染物排放 . 9 1.3 上海市工业能源现状 . 11 1.3.1 工业发展规模 . 11 1.3.2 工业能源消耗 . 12 1.3.3 工业污染物排放 . 14 1.4 能源生产力研究现状 . 15 1.4.1 能源生产力概念 . 15 1.4.2 能源生产力国际比较 . 17 1.4.3 主要国家能源生产力发展 . 18 1.5 本报告工作简介 . 20 1.5.1 研究意义 . 20 1.5.2 研究内容 . 21 第二章 研究方法与文献综述 . 23 2.1 相关文献综述 . 23 2.1.1 能源生产力研究方法 .