摘 要

随着全球能源需求的持续增长和环境保护意识的提升，天然气作为一种相对清洁的能源，逐渐在全球范围内得到广泛应用。近年来，随着经济的快速发展和工业化进程的推进，我国天然气消费持续增长。基于全球气候治理的责任和可持续发展的内在需求，我国明确提出“力争2030年前实现碳达峰、2060年前实现碳中和”的发展目标。基于此背景，深入研究我国天然气消费的变化趋势及其影响因素，对于促进能源结构调整、优化能源消费模式、实现可持续发展具有重要的现实意义。

本文基于天然气消费终端视角，从农林牧渔业、工业和建筑业等七大行业出发，研究我国天然气消费发展趋势。首先，对本文所涉及的基本概念和理论基础进行界定；其次，分析了我国经济社会发展和能源消费总量、天然气消费量等具体变化现状；之后利用LMDI模型将影响我国2005-2022年天然气消费的因素分解为非可再生清洁能耗结构效应、清洁能耗结构效应、行业能耗结构效应、能源强度效应、经济发展效应和人口规模效应六大效应，并量化出各分解效应对我国天然气消费的逐年贡献值，指出非可再生清洁能耗结构效应、清洁能耗效应和经济增长效应会正向促进我国天然气消费发展，而能源强度效应则起负向抑制作用；同时，进一步分析了此四大关键效应下各行业的影响贡献值；紧接着基于LMDI的因素分解分析结果，构建出包括经济、能源和技术多个子系统的系统动力学模型对我国天然气消费进行仿真模拟；最终，利用情景分析法设计出6种我国天然气消费变化的具体情景模式：基准情景、政策情景、经济衰退情景、技术创新驱动情景、快速能源转型情景和碳中和情景；最终利用构建出的系统动力学模型仿真模拟出我国2023-2035年各情景下天然气消费总量、天然气消费占比和天然气行业消费结构三大变化情况，预测结果表明：经济衰退情景下天然气消费的增速最慢；同时，技术创新驱动情景和碳中和情景下的天然气消费呈现出增长趋势，但后期能源消费总量增速不断放缓。且通过对不同情景下我国天然气消费分行业的预测可知，工业和居民生活领域始终是天然气消费的主力军。

最后，基于我国天然气消费的因素分解和多情景预测结果，并结合我国经济社会和天然气消费现状，本文从政策、技术、产业和行业等多角度出发，给出促进我国未来天然气消费发展的对策建议：（一）推动能源结构优化，增加清洁能源比例；（二）加强能源效率提升，推动工业和建筑业节能；（三）支持技术创新，提升低碳转型能力；（四）优化天然气基础设施建设，推动绿色交通与清洁生产；（五）加强交通运输领域的清洁能源应用；（六）促进居民生活领域清洁能源应用。

With the continuous growth of global energy demand and increasing awareness of environmental protection, natural gas, as a relatively clean energy source, has gradually become more widely used worldwide. In recent years, driven by rapid economic development and industrialization, China’s natural gas consumption has continued to rise. In response to global climate governance responsibilities and the inherent need for sustainable development, China has set a clear goal: "to achieve carbon peak by 2030 and carbon neutrality by 2060." Against this backdrop, it is of great practical significance to conduct in-depth research on the trends in China’s natural gas consumption and its influencing factors, as this can help promote energy structure adjustments, optimize energy consumption patterns, and achieve sustainable development.

This paper examines the development trends of natural gas consumption in China from the perspective of its end-use sectors, including agriculture, forestry, animal husbandry, fishery, industry, and construction. First, the basic concepts and theoretical foundations are defined. Next, an analysis is made of the current situation in China’s economic and social development, along with specific changes in total energy consumption and natural gas consumption. Subsequently, the factors affecting China’s natural gas consumption from 2005 to 2022 are broken down into six major effects: non-renewable clean energy consumption structure, clean energy consumption structure, industrial energy consumption structure, energy intensity, economic development, and population size. The annual contribution of each effect to China’s natural gas consumption is quantified, highlighting that the non-renewable clean energy consumption structure, clean energy consumption, and economic growth effects positively drive natural gas consumption growth, while the energy intensity effect exerts a negative inhibitory impact. Further analysis is conducted to examine the contribution of each industry under these key effects.

Building on the results of the factor decomposition, a system dynamics model is constructed, encompassing subsystems such as the economy, energy, and technology, to simulate natural gas consumption in China. Using scenario analysis, six scenarios for the future development of China’s natural gas consumption are designed: baseline, policy, recession, technological innovation-driven, rapid energy transition, and carbon neutrality. The system dynamics model is then used to simulate changes in total natural gas consumption, the proportion of natural gas in total consumption, and the consumption structure of the natural gas industry from 2023 to 2035. The results show that the recession scenario exhibits the slowest growth rate for natural gas consumption. At the same time, both the technological innovation-driven and carbon neutrality scenarios show an upward trend in natural gas consumption, although the growth rate of total energy consumption slows down significantly in later stages. Industry-specific predictions under various scenarios indicate that the industrial and residential sectors remain the primary drivers of natural gas consumption.

Finally, based on the factor decomposition of China’s natural gas consumption and the results from multi-scenario predictions, and considering the current state of China’s economy and natural gas consumption, this paper offers several policy recommendations to promote the future development of natural gas consumption: (1) Optimize the energy structure by increasing the share of clean energy. (2) Enhance energy efficiency, particularly in the industrial and construction sectors, to promote energy conservation. (3) Support technological innovation to strengthen low-carbon transformation capabilities. (4) Improve natural gas infrastructure and promote green transportation and clean production. (5) Boost the use of clean energy in the transportation sector. (6) Encourage the adoption of clean energy in residential areas.

[1] Kraft J, Kraft A. Relationship between energy and GNP[J]. Journal of Energy Finance & Development, 1978,3: 401-403.

[2] Yu ESH, Choi JY. Causal relationship between energy and GNP: an international Comparison[J]. The Journal of Energy and Development ,1985,10(2):249-272.

[3] Apergis N, Payne EJ. Natural gas consumption and economic growth: A panel investigation of 67 countries[J]. Applied Energy,2010,87(8):2759-2763.

[4] Kum H, Ocal O, Aslan A. The relationship among natural gas energy consumption, capital and economic growth: Bootstrap-corrected causality tests from G7 countries[J]. Renewable and Sustainable Energy Reviews,2012,16(5):2361-2365.

[5] Das A, McFarlane AA, Chowdhury M. The dynamics of natural gas consumption and GDP in Bangladesh[J]. Renewable and Sustainable Energy Reviews,2013,22:269-274.

[6] Aydin M. Natural gas consumption and economic growth nexus for top 10 natural Gas–Consuming countries: A granger causality analysis in the frequency domain[J]. Energy,2018,165:179-186.

[7] Kani H A, Abbasspour M, Abedi Z. Estimation of demand function for natural gas in Iran: Evidences based on smooth transition regression models[J]. Economic Modelling,2014,36(1):341-347.

[8] Chai J, Liang T, Lai K, et al. The future natural gas consumption in China: Based on the LMDI-STIRPAT-PLSR framework and scenario analysis[J]. Energy Policy,2018,119:215-225.

[9] Wang J, Li N. Influencing factors and future trends of natural gas demand in the eastern, central and western areas of China based on the grey model[J]. Natural Gas Industry B,2020,7(5):473-483.

[10] Xiao Y, Li K, Hu Y, et al. Combining STRIPAT model and gated recurrent unit for forecasting nature gas consumption of China[J]. Mitigation and Adaptation Strategies for Global Change,2020,25(7):1-19.

[11] Jiang H, Dong X, Jiang Q, et al. What drives China's natural gas consumption? Analysis of national and regional estimates[J]. Energy Economics, 2020, 87:104744.

[12] Yousaf R M, Lin B. Natural gas consumption, energy efficiency and low carbon transition in Pakistan[J]. Energy,2022,240.

[13] Qiao W, Liu W, Liu A. A combination model based on wavelet transform for predicting the difference between monthly natural gas production and consumption of U.S[J]. Energy,2021,235.

[14] Liu J, Wang S, Wei N, et al. Natural gas consumption forecasting: A discussion on forecasting history and future challenges[J]. Journal of Natural Gas Science and Engineering,2021,90:103930.

[15] Wang J, Jiang H, Zhou Q, et al. China’s natural gas production and consumption analysis based on the multicycle Hubbert model and rolling Grey model[J]. Renewable and Sustainable Energy Reviews,2016,53:1149-1167.

[16] Xu G, Wang W. Forecasting China’s natural gas consumption based on a combination model[J]. Journal of Natural Gas Chemistry,2010,19(5):493-496.

[17] Lin B, Wang T. Forecasting natural gas supply in China: Production peak and import trends[J]. Energy Policy,2012,49:225-233.

[18] Melikoglu M. Vision 2023: forecasting Turkey’s natural gas demand between 2013 and 2030[J]. Renewable and Sustainable Energy Reviews,2013;22:393-400.

[19] Xu G, Chen Y, Yang M, et al. An outlook analysis on China’s natural gas consumption forecast by 2035: Applying a seasonal forecasting method[J]. Energy,2023,284:128602.

[20] Qian W, Zhang H, Sui A, et al. A novel adaptive discrete grey prediction model for forecasting development in energy consumption structure—from the perspective of compositional data[J]. Grey Systems: Theory and Application,2022,12(3):672-697.

[21] Ding S. A novel self-adapting intelligent grey model for forecasting China's natural-gas demand[J]. Energy,2018,162:393-407.

[22] Zheng C, Wu W, Jiang J, et al. Forecasting natural gas consumption of China using a novel grey model[J]. Complexity,2020,2020:1-9.

[23] Li N, Wang J, Wu L, et al. Predicting monthly natural gas production in China using a novel grey seasonal model with particle swarm optimization[J]. Energy,2021,215:119118.

[24] Szoplik J. Forecasting of natural gas consumption with artificial neural networks[J]. Energy,2015,85:208-220.

[25] Bai S, Huang X, Luo M, et al. Deep hybrid models for daily natural gas consumption forecasting and complexity measuring[J]. Energy Science & Engineering,2022,11(2):654-674.

[26] Li Z, Liu L, Qiao W. Short-term natural gas consumption prediction based on wavelet transform and bidirectional long short-term memory optimized by Bayesian network[J]. Energy Science & Engineering, 2022;10(9):3281–300.

[27] Ceylan Z. Comparative analysis of deep learning and classical time series methods to forecast natural gas demand during COVID-19 pandemic[J]. Energy sources, Part B. Economics, planning, and policy, 2023, 18(1):1-21.

[28] Liu J, Tian S, Wang Q, et al. The regulation path of coal consumption based on the total reduction index–a case study in Shandong Province, China[J]. Energy,2023,262:125591.

[29] Chi K, Nuttall W, Reiner D. Dynamics of the UK Natural Gas Industry: System Dynamics Modelling and Long-Term Energy Policy Analysis[J]. Technological Forecasting & Social Change,2009,76(922):339-357.

[30] Sun J, Lv D, Fan W. The simulated system dynamics analysis of the natural gas supply and demand[J]. Kybernetes,2010,39(8):1262-1269.

[31] Kiani B, Pourfakhraei M. A system dynamic model for production and consumption policy in Iran oil and gas sector[J]. Energy Policy, 2010, 38(12):7764-7774.

[32] Mu X, Li G, Hu G. Modeling and scenario prediction of a natural gas demand system based on a system dynamics method[J]. Petroleum Science, 2018, 15(04):234-246.

[33] Hong Z, Sha H, Qian S. A system dynamic model for natural gas safety supply and demand balance mechanism[J]. Energy Science & Engineering,2023,11(10):3768-3779.

[34] 卢全莹,柴建,朱青,等.天然气消费需求分析及预测[J].中国管理科学,2015,23(S1):823-829.

[35] 徐国政.基于LMDI的中国天然气消费影响因素研究[J].中国煤炭,2016,42(5):32-37.

[36] 郑明贵,王萍,钟聪红.2020—2030年中国天然气需求预测[J].中国矿业,2021,30(2):7-13.

[37] 张同蕾.“双碳”目标下中国天然气消费影响机制研究[J].上海节能,2024,(5):743-751.

[38] 李洪兵,韩咪,刘可,等.中国城市天然气需求趋势预测[J].油气与新能源,2024,36(3):6-13.

[39] 成金华,刘伦,王小林,等.天然气区域市场需求弹性差异性分析及价格规制影响研究[J].中国人口·资源与环境,2014,24(08):131-140.

[40] 杨俊,牛迪.我国天然气消费的区域差异及影响因素—基于省际面板数据的实证分析[J].新疆大学学报,2016,44(2):1-9.

[41] 朱春红.中国天然气消费区域差异研究[D].中国石油大学(华东),2017.

[42] 柴建,毛敏.不同省份和区域间天然气需求驱动路径及差异性分析—基于贝叶斯结构方程模型[J].系统科学与数学,2020,40(12):2431-2444.

[43] 李洪兵,刘可,吴小东,等.碳中和背景下四川省天然气消费影响因素分析及未来消费预测[J].中国矿业,2024,33(12):111-120.

[44] 甄仟,郭晓茜,闫强.基于行业视角的中国天然气消费因素分解[J].中国矿业,2018,27(2):50-57.

[45] 柴建,赵逸杰,梁婷,等.中国非居民用户用气量的行业异质性动态分析及预测—基于一种新的综合集成分析预测模型框架[J].系统科学与数学,2022,42(2):318-336.

[46] 张晶.我国中长期天然气需求量预测方法研究[D].中国石油大学(北京),2023.

[47] 宋超,宋娟.基于遗传算法优化和BP神经网络的短期天然气负荷预测[J].工业控制计算机,2012,25(10):82-84.

[48] 高俊莲,徐向阳.基于ARIMA模型的中国天然气消费量预测与分析[J].数学的实践与认识,2015,45(9):44-50.

[49] 张涛,杨娇敏.我国区域天然气消费量的趋势预测[J].天然气工业,2016,36(9):135-140.

[50] 宋超,宋娟,任军.基于Elman神经网络的短期天然气负荷预测建模与仿真[J].工业控制计算机,2016,29(04):87-88.

[51] 仝延增,陈海俊,张晓蒙,等.基于FGM(1,1)模型的北京市天然气消费量预测[J].数学的实践与认识,2020,50(3):79-83.

[52] 李洪兵,张吉军,韩咪,等.改进模型的城市天然气消费量预测研究[J].城市燃气,2021,(3):42-47.

[53] 刘真,潘文菊,刘佳,等.基于K-means聚类+灰色理论+BP神经网络的区域天然气长期需求预测[J].油气储运,2024,43(1):103-110.

[54] 叶倩,陈晓慧,谢扬.基于通径分析的重庆市天然气消费量影响因素分析及预测[J].机械,2010,37(09):1-4.

[55] 邹绍辉,丁治立.基于DDE-BAG的中国天然气需求预测模型[J].中国矿业,2018,27(08):62-69.

[56] 李洪兵,张吉军.天然气需求影响因素分析及未来需求预测[J].运筹与管理,2021,30(9):132-138.

[57] 杜学平,郎智凯,柳梦琳,等.中国经济驱动型省级天然气负荷的预测模型[J].油气储运,2023,42(10):1184-1192+1200.

[58] 李君臣,董秀成,高建.我国天然气消费的系统动力学预测与分析[J].天然气工业,2010,30(4):127-129+151.

[59] 穆献中,李国昊.基于系统动力学模型的中国天然气需求情景预测及影响因素研究[J].工程研究-跨学科视野中的工程,2018,10(1):56-67.

[60] 丁聿,付佳鑫,唐旭,等.基于SD-GIS方法的天然气需求量中长期变化趋势及其空间格局预测—以北京市为例[J].天然气工业,2021,41(4):176-185.

[61] 孟亚东,龚继忠.我国天然气需求的系统动力学预测与分析[J].价值工程,2023,42(33):7-9.

[62] 毛宗学.“双碳”约束下中国天然气消费预测研究[J].中国高新科技,2024,(15):135-136+145.

[63] 陈红仙.基于系统动力学的中国天然气需求预测与分析[D].中国地质大学(北京),2016.

[64] 孙慧.我国天然气产业结构分析与优化升级研究[D].中国地质大学(北京),2018.

[65] 范静静,王建良,黄辉,等.“双碳”目标下中国天然气发展定位及潜在路径研究[J].煤炭经济研究,2022,42(8):38-47.

[66] 李乔楚,张鹏,罗平亚.“双碳”目标下四川省天然气产业低碳发展策略研究——集成系统动力学和清单算法[J].科技管理研究,2024,44(23):197-206.

[67] 宋鹏飞,张超,侯建国,等.碳约束条件下天然气化工技术发展及碳减排量预测[J].低碳化学与化工,2023,48(2):71-77.

[68] 黎园.我国天然气化工产业发展现状及前景分析[J].低碳化学与化工,2024,49(1):105-112.

[69] 董康银.低碳约束背景下中国能源转型路径与优化模型研究[D].中国石油大学(北京),2019.

[70] 杨泽.“双碳”目标与水资源约束下山西能源转型研究[D].山西大学,2023.

[71] Jette S. The Third Industrial Revolution-How Lateral Power Is Transforming Energy, The Economy, and the World[J]. Survival, 2011,2(2):67-68.

[72] 柳逸月.中国能源系统转型及可再生能源消纳路径研究[D].兰州大学,2017.

[73] 周淑慧,郝迎鹏,沈鑫,等.对天然气在新型能源体系中地位和作用的认识[J].国际石油经济,2024,32(01):2-16.

[74] 李宗才.我国低碳经济研究述评[J].学术界,2010,(6):215-219+289.

[75] 王宗军,肖仁桥,田原.低碳经济发展研究述评[J].技术经济,2012,31(4):5-9+16.

[76] 余信权.从低碳经济论环境保护[J].江西化工,2017,(5):179-181.

[77] Zhang L, Diao G, You K. Toward a low-carbon economy: Insights from low- carbon complexity index[J]. Environmental Impact Assessment Review,2025,112107856-107856.

[78] 杜小云.我国低碳城市建设水平时空演变规律及提升策略研究[D].重庆大学,2021.

[79] You L, Liu S, Tang T, et al. China’s Low-Carbon Economy: Index Measurement, Regional Differences, and Spatial Correlation[J]. Journal of Applied Economics and Policy Studies,2024,5(1):102-112.

[80] United Nations U N. Transforming our world: the 2030 Agenda for Sustainable Development[J]. Working Papers, 2015.

[81] 顾肃.可持续发展的伦理价值反思[J].学术界,2023,38(7):42-49.

[82] 侍剑峰.基于系统动力学的中国碳排放峰值预测及应对策略研究[D].华北电力大学,2018.

[83] 李强.可持续发展概念的演变及其内涵[J].生态经济,2011,(07):87-90.

[84] Visser W, Brundtland G H. Our Common Future (‘The Brundtland Report’): World Commission on Environment and Development[J]. The Top 50 Sustainability Books,2009，(4):52-55.

[85] 牛文元.中国可持续发展的理论与实践[J].中国科学院院刊,2012,27(3):280-289.

[86] 吴雅.低碳城市建设的演变规律及提升路径设计研究[D].重庆大学,2019.

[87] 高国荣.中国共产党与中国生态文明建设[J].近代史研究,2024,(6):4-24+158.

[88] 刘宇炫,王毅.绿色转型引领发展的特征、挑战与取向[J].生态文明研究,2024,(6):15-29.

[89] Ang B. The LMDI approach to decomposition analysis: a practical guide[J]. Energy Policy,2003,33(7):867-871.

[90] Hao J, Gao F, Fang X, et al. Multi-factor decomposition and multi-scenario prediction decoupling analysis of China’s carbon emission under dual carbon goal[J]. The Science of the total environment, 2022, 841:156788.

[91] 王其藩.高级系统动力学[M].清华大学出版社,1995.

[92] 赵红凯.河北省天然气消费影响因素及预测研究[D].华北电力大学,2021.

[93] 曾庆贺.天然气市场需求的系统动力学模型研究[D].中国石油大学(北京),2022.

[94] 国家信息中心.中国经济社会发展的中长期目标、战略与路径[EB/OL].(2021-2-7) [2025-1-3]. https://www.efchina.org/Reports-zh/report-lceg-20210207-4-zh.

[95] 尹伟华.“十四五”时期我国产业结构变动特征及趋势展望[J].中国物价,2021,(9):3-6.

[96] 徐辉,王慧.产业结构调整对中国碳强度目标实现的影响:展望2020年[J].科技管理研究,2016,36(13):232-236.

[97] 国家发展和改革委员会, 国家能源局. “十四五”现代能源体系规划[EB/OL]. (2022-01-29) [2025-1-3]. https://www.gov.cn/xinwen/2017-04/25/content_5230568.htm.

[98] 国家发展和改革委员会,国家能源局.能源生产和消费革命战略(2016-2030)[EB/OL]. (2016-12-29) [2025-1-3]. https://www.gov.cn/xinwen/2017-04/25/content_5230568.htm.