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喀斯特地区农村能源消费特征及影响因素
王琦1, 熊康宁2,3, 盈斌2,3, 张兴菊4, 任威1
1.贵州师范大学喀斯特研究院,贵阳550001;2.1.贵州师范大学喀斯特研究院,贵阳550001;3.2.国家喀斯特石漠化防治工程技术研究中心,贵阳550001;4.贵州师范大学地理与环境科学学院,贵阳550025
摘要:
[目的]喀斯特农村地区低值粗放的能源利用方式对原本脆弱的生态环境产生强烈胁迫,薪柴过度使用造成植被破坏,从而加剧土壤侵蚀,成为诱发石漠化的主要因素之一。明确不同喀斯特地区农村能源消费特征及影响因素以促进能源结构优化是喀斯特生态系统恢复的重要措施,也是社会—生态研究领域的一大议题。[方法]采用随机抽样的方式调查喀斯特峡谷、高原山地和槽谷3种不同地貌类型的石漠化治理示范区的能源结构特征与消费现状,利用Tobit和Logit模型对能源消费的影响因素进行回归分析。[结果](1)喀斯特地区用能结构单一,电力使用率达9931%,但对薪柴的替代进程缓慢,薪柴使用率仍高达8310%,其他能源使用率均不足50%,不同地貌区能源结构差异显著。(2)喀斯特地区农村能源消费量整体偏低且以薪柴消费为主导,人均能源消费量仅占全国人均消费量的4032%,薪柴消费量占总能耗的5069%,其余能源均不足20%,不同地貌区能源消费差异明显。(3)家庭人口数量、年纯收入、养殖牲畜数量、耕地面积、煤矿以及政策对喀斯特地区农村能源的消费具有显著影响,能源政策可以有效促进新能源使用。[结论]针对喀斯特峡谷区、高原山地区和槽谷区依次提出能源服务配套型、金融贷款合作型和企业入驻引资型3种模式建议,对优化喀斯特地区能源结构、保障石漠化治理成果具有一定的现实意义。
关键词:  农村能源喀斯特石漠化消费结构影响因素Tobit模型
DOI:
分类号:F3232
基金项目:国家重点研发计划项目“喀斯特高原峡谷石漠化综合治理与生态产业规模经营技术与示范”(2016YFC0502603); 贵州省科技计划项目“喀斯特石漠化地区生物质能消费足迹承载力研究”(黔科合LH[2017]7365); 贵州省研究生教育创新计划项目“贵州省熊康宁喀斯特环境研究生导师工作室建设”(黔教研合GZS字[2016]04号); 贵州师范大学博士科研启动基金项目“喀斯特石漠化山区农村能源结构生态经济耦合特征与优化”(0514168)
CONSUMPTION CHARACTERISTICS OF RURAL ENERGIES IN KARST AREA AND THEIR INFLUENCING FACTORS
Wang Qi1, Xiong Kangning2,3, Ying Bin2,3, Zhang Xingju4, Ren Wei1
1.School of Karst Science,Guizhou Normal University,Guiyang,Guizhou 550001,China;2.1.School of Karst Science,Guizhou Normal University,Guiyang,Guizhou 550001,China;3.2. State Engineering Technology Institute for Karst Desertification Control,Guiyang,Guizhou 550001,China;4.School of Geography and Environmental Science,Guizhou Normal University,Guiyang,Guizhou 550025,China
Abstract:
Low level and extensive use of energy strongly threatens the already fragile eco environment in karst rural area. Also, the overuse of fuelwood has led to the destruction of vegetation and therefore intensifies the erosion of soil, being one of the main factors resulting in rocky desertification issues. Clarifying the characteristics of rural energy consumption and its influential factors to optimize the structure of energy is an important measure to promote the restoration of karst ecosystems, and it is also a hot issue discussed in socio ecological research. Random sampling method was adopted to investigate the characteristics of energy structure and the status of energy consumption in three areas demonstrating the control situation of rocky desertification issues and representing three different landforms including karst gorges, karst plateaus and mountains, as well as karst valleys. Moreover, models of Tobit and Logit were taken to perform regression analysis on factors affecting energy consumption. The results show that (1) The utilization structure of energy in karst area is simple, with utilization ratio of electricity reaching 99.31%. However, the process of replacing fuelwood with electricity is slow, and the use of fuelwood still accounts for 83.10%. The ratios of other utilized energies are less than 50%. The structures of energy utilization in areas with different landforms are also various. (2) The consumption of rural energy in karst area is low and is dominated by fuelwood consumption, and per capita energy consumption there represents only 40.32% of national per capita consumption. The consumption of fuelwood accounts for 50.69% of total energy consumption, while other energies are less than 20%. The energy consumptions of different landforms vary obviously. (3) The population of a family, annual net income, the number of livestock, the area of cultivated land, coal mines and policies significantly affect rural energy consumption in karst area. In addition, energy policies can promote the use of new energy. In light of the actual situations of karst gorges, karst plateaus and mountains as well as karst valleys, three suggested modes are proposed, including energy service supporting mode, financial loan cooperation mode and enterprise investment mode. It is of practical significance to the promotion of the optimization of energy structure and the conservation of the achievements of rocky desertification control in karst areas.
Key words:  rural energy  karst  rocky desertification  consumption structure  influencing factors  Tobit model