全球气候变暖对人类的生存和发展造成巨大威胁。改革开放以来，我国能源消耗呈持续增长态势，导致碳排放量持续增大，目前已成为全球碳排放量最高的国家之一。陕西省作为能源资源较为丰富的地区，其经济发展与能源消耗关系密切，实现低碳发展目标方面面临着巨大挑战。土地利用是区域碳排放的重要方面，县域尺度土地利用碳排放研究有助于揭示区域内部碳排放的异质性。因此，本文从县域尺度开展陕西省土地利用碳排放时空特征及影响因素研究，以期为科学认识陕西省土地利用碳排放演变过程及实现碳达峰、碳中和目标提供支持。

本文基于土地利用数据、能源消耗数据、夜间灯光数据和统计数据，采用趋势分析方法、空间自相关、马尔科夫链、空间马尔科夫链和地理探测器等方法，开展了2000-2020年陕西省土地利用变化、土地利用碳排放量核算、县域土地利用碳排放时空变化和影响因素分析的研究，为陕西省县域土地利用低碳减排提出建议。主要结论如下：

（1）2000-2020年陕西省土地利用变化呈现出林地、草地、水域和建设用地增加而耕地和未利用地减少的特点。林地和建设用地增加最多，其中转入林地最多的地类是草地，主要集中在陕南县区，其次是耕地，主要集中在延安市北部县区；建设用地的增加主要来源于耕地的转入，主要集中在关中平原县区。耕地减少最多，主要转为林地、草地和建设用地，减少区域主要集中在榆林市、延安市北部县区和各市辖区。2000-2020年陕西省夜间灯光总亮度值与相应年份的省级能源消耗碳排放量之间呈高度正向相关性，两者线性拟合方程R²为0.8604，拟合方程得到的能源消耗碳排放量模拟值与统计值之间的平均相对误差为15.16%，由此可见，建立的拟合模型精度较高。

（2）2000-2020年陕西省土地利用碳排放量呈持续增长趋势，从2000年的1566.48万吨增长到2020年的7132.14万吨，其增长趋势具有一定的阶段性。从县域时间演变的视角来看，陕北地区能源丰富的县区土地利用碳排放量增长速度最快，其次是关中地区的西安市各市辖区。将不同县区在各年份的土地利用碳排放量划分为5个等级，其中极重度、重度和中度碳排放等级的县区数量逐年增多，轻度碳排放和负碳排放等级的县区数量逐年减少，县域土地利用碳排放量空间分布呈“陕北>关中>陕南”的格局，极重度碳排放等级的县区多集中在西安市及其周边人口密集、经济发达的县区和陕北地区能源丰富的县区。

（3）2000-2020年陕西省不同县区土地利用碳排放的空间正相关性较强，全局空间自相关Moran’s I值均大于0.5且呈先增后减的态势，但空间上总体呈显著聚集状态。局部自相关较为稳定，以高-高聚集和低-低聚集为主，前者主要集中在西安市、咸阳市主城区和陕北地区能源丰富的县区，后者主要集中在陕南地区且县区数量呈不断减少的趋势。将陕西省县域土地利用碳排放量按照五分位点（0.2/0.4/0.6/0.8）划分为5种类型，由马尔科夫转移概率矩阵可知，各县区保持碳排放类型不变的稳定性较强。区域背景与邻域状态两种空间滞后条件对县域土地利用碳排放类型的稳定性和转移方向有所影响，区域背景对县域土地利用碳排放“俱乐部趋同”现象具有一定的强化作用，而邻域状态能够增加各县区碳排放类型转移的活跃性。

（4）利用地理探测器对2000-2020年陕西省县域土地利用碳排放影响因素进行分析，研究发现经济发展水平、产业发展水平、人口规模、城市发展水平对县域土地利用碳排放影响较大，且任意两因素之间交互作用产生的影响力比单独因素产生的影响力更大。最后，基于研究结论提出建议：优化人口结构，贯彻城市发展低碳理念；改善土地利用结构，提升土地集约利用水平；提高能源利用效率，优化产业结构；深化政府职能，探索低碳路径。

Global warming poses a huge threat to human survival and development. Since the reform and opening up, China's energy consumption has shown continuous growth, resulting in a continuous increase in carbon emissions, and has now become one of the countries with the highest carbon emissions in the world. Shaanxi Province, as a region rich in energy resources, has a close relationship between its economic development and energy consumption, and faces great challenges in achieving low-carbon development goals. Land use is an important aspect of regional carbon emissions, and research on carbon emissions form county land use can help reveal the heterogeneity of carbon emissions within the region.. Therefore, this paper carries out a study on the spatiotemporal characteristics and influencing factors of carbon emissions form county land use in Shaanxi Province, with a view to providing support for the scientific understanding of the evolution of carbon emissions form land use in Shaanxi Province and the realization of the goal of carbon peaking and carbon neutrality.

Based on land use data, energy consumption data, nighttime light data and statistical data, this paper carries out a study on land use changes, accounting of carbon emissions from land use, spatiotemporal changes in carbon emissions from county land use and analysis of influencing factors in Shaanxi Province from 2000 to 2020 by adopting the methods of trend analysis, spatial autocorrelation, Markov chain, spatial Markov chain and GeoDetector， and makes suggestions for the Shaanxi Province county land use low-carbon emission reduction suggestions. The main conclusions are as follows:

(1) From 2000 to 2020, the land use change in Shaanxi Province were characterized by an increase in forest land, grassland, water area and construction land, while cultivated land and unused land decreased. The greatest increases were seen in forest land and construction land, with the land type most transferred to forest land being grassland, which was mainly concentrated in southern Shaanxi counties and districts, followed by cultivated, which was mainly concentrated in the northern counties and districts of Yan'an city; The increase in construction land mainly comes from the conversion of cultivated land, which is mainly concentrated in the Guanzhong Plain counties. Cultivated land decreased the most, mainly converted to forest land, grassland and construction land, with the decrease mainly concentrated in the counties of Yulin City, the northern counties of Yan'an City, and the municipal districts. From 2000 to 2020, the total DN value of nighttime light in Shaanxi Province showed a high positive correlation with the corresponding annual provincial energy consumption carbon emissions, with a linear fitting equation R² of 0.8604, and the average relative error between the simulated and statistical values of carbon emissions from energy consumption obtained from the fitted equations is 15.16%.

(2) Carbon emissions from land use in Shaanxi Province from 2000 to 2020 showed a continuous growth trend, the carbon emissions form land use in Shaanxi Province showed a continuous increasing trend during the study period, growing from 15.6648 million tons in 2000 to 71.3214 million tons in 2020, and this growth trend has certain stage. From the perspective of temporal evolution at the county level, energy-rich counties and districts in northern Shaanxi have the fastest growth rate of land-use carbon emissions, followed by the municipal districts of Xi'an in Guanzhong region. The land use carbon emissions of different counties and districts in each year were categorized into five classes, in which the number of counties and districts with extremely heavy, heavy and moderate carbon emissions increased year by year, while the number of counties with light carbon emissions and negative carbon emissions decreased year by year, and the spatial distribution of carbon emissions form county land use presents a pattern of “Northern Shaanxi>Guanzhong>Southern Shaanxi”, with counties with extremely heavy carbon emissions concentrated in the populous, economically developed areas of Xi'an City and its surrounding areas and the energy-rich areas of Northern Shaanxi.

(3) The spatial positive correlation of carbon emissions from land use in different counties and districts in Shaanxi Province from 2000 to 2020 was strong, and the global spatial autocorrelation Moran's I values are all greater than 0.5 and show the trend of increasing first and then decreasing, but the overall spatial aggregation is significant. The local spatial autocorrelation is relatively stable, dominated by High-High aggregation and Low-Low aggregation, with the former mainly concentrated in the main urban areas of Xi'an and Xianyang cities and the energy-rich counties in Northern Shaanxi, and the latter primarily concentrated in Southern Shaanxi, and showing a decreasing trend. The carbon emissions from county land use in Shaanxi Province were divided into five types according to quintiles (0.2/0.4/0.6/0.8), and the Markov transfer probability matrix shows that the counties and districts are more stable in keeping the carbon emission types unchanged. Two spatial lag conditions, regional background and neighboring state, have an impact on the stability and direction of transfer of county land use carbon emission types.The regional background has a certain reinforcing effect on the "club convergence" phenomenon of carbon emissions form county land use, while the neighboring state can increase the activity of carbon emission type transitions among the counties.

(4) Using geographic detectors to analyze the factors affecting carbon emissions from land use in counties in Shaanxi Province from 2000 to 2020, the study concludes that the level of economic development, the level of industrial development, population size, and the level of urban development have a greater impact on carbon emissions from county land use, and the impact generated by the interaction between any two factors was greater than the impact produced by any single factor alone. Finally, based on the research conclusions, the following suggestions are proposed: optimize the population structure and implement the concept of low-carbon urban development; improve the land use structure and enhance the level of land intensive use; improve energy utilization efficiency and optimize industrial structure; deepen government functions and explore the low-carbon pathways.

[1] Lade S J, Steffen W, Vries W D, et al. Human impacts on planetary boundaries amplified by Earth system interactions[J]. Nature Sustainability, 2020, 3(2): 119-128.

[2] Steffen W, Richardson K, Rockstrom J, et al. Planetary boundaries: Guiding human development on a changing planet[J]. Science, 2015, 347(6223): 97-158.

[3] 习近平. 在第七十五届联合国大会一般性辩论上的讲话[N]. 人民日报, 2020-09-23(3).

[4] Yang H, Huang J, Liu D. Linking climate change and socioeconomic development to urban land use simulation: Analysis of their concurrent effects on carbon storage[J]. Applied Geography, 2020, 115(8): 102135.

[5] 韩骥, 周翔, 象伟宁. 土地利用碳排放效应及其低碳管理研究进展[J]. 生态学报, 2016, 36(4): 1152-1161.

[6] 黄贤金, 张秀英, 卢学鹤, 等. 面向碳中和的中国低碳国土开发利用[J]. 自然资源学报, 2021, 36(12): 2995-3006.

[7] Houghton R A, House J I, Pongratz J, et al. Carbon emissions from land use and land-cover change[J]. Biogeosciences, 2012, 9(12): 5125-5142.

[8] 赵荣钦, 陈志刚, 黄贤金, 等. 南京大学土地利用碳排放研究进展[J]. 地理科学, 2012, 32(12): 1473-1480.

[9] 田华征, 马丽. 中国工业碳排放强度变化的结构因素解析[J]. 自然资源学报, 2020, 35(3): 639-653.

[10] Feng K, Hubacek K, Guan D. Lifestyles, technology and CO2 emissions in China: A regional comparative analysis[J]. Ecological Economics, 2009, 69(1): 145-154.

[11] 李颖, 黄贤金, 甄峰. 江苏省区域不同土地利用方式的碳排放效应分析[J]. 农业工程学报, 2008, 24(S2): 102-107.

[12] Houghton R A. Tropical deforestation and atmospheric carbon dioxide[J]. Climatic Change, 1991, 19: 99-118.

[13] Houghton R A, Hackler J L. Emissions of carbon from forestry and land-use change in tropical Asia[J]. Global Change Biology, 1999, 5(4): 481-492.

[14] Houghton R A, Hackler J L. Sources and sinks of carbon from land-use change in China[J]. Global Biogeochemical Cycles, 2003, 17(2): 1034.

[15] IPCC. Climate Change 2006: the Physical Science Basis.Contribution of Working Group l to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change[S]. Cambridge: Cambridge University Press, 2007.

[16] IPCC. Climate Change 1996: the Physical Science Basis.Contribution of Working Group l to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change[S]. Cambridge: Cambridge University Press, 1997.

[17] 中华人民共和国国家发展与改革委员会应对气候变化司. 中华人民共和国气候变化初始国家信息通报[M]. 北京: 中国计划出版社, 2005.

[18] 中华人民共和国国家发展与改革委员会应对气候变化司. 中华人民共和国气候变化第二次国家信息通报[M]. 北京: 中国经济出版社, 2013.

[19] Li J S, Guo X M, Chuai X W, et al. Reexamine China's terrestrial ecosystem carbon balance under land use-type and climate change[J]. Land Use Policy, 2021, 102(6237): 105275.

[20] Hutyra L R, Yoon B, Hepinstall-Cymerman J, et al. Carbon consequences of land cover change and expansion of urban lands: A case study in the Seattle metropolitan region[J]. Landscape and Urban Planning, 2011, 103(1): 83-93.

[21] Leija-Loredo E G, Pavón N P, Sánchez-González A, et al. Land cover change and carbon stores in a tropical montane cloud forest in the Sierra Madre Oriental, Mexico[J]. Journal of Mountain Science, 2018, 15(10): 2136-2147.

[22] Ni J. Carbon Storage in Terrestrial Ecosystems of China: Estimates at Different Spatial Resolutions and Their Responses to Climate Change[J]. Climatic Change, 2001, 49(3): 339-358.

[23] Kauppi P E, Mielikäinen K, Kuusela K. Biomass and Carbon Budget of European Forests, 1971 to 1990[J]. Science, 1992, 256(5053): 70-74.

[24] 刘国华, 傅伯杰, 方精云. 中国森林碳动态及其对全球碳平衡的贡献[J]. 生态学报, 2000, 20(5): 733-740.

[25] 方精云, 郭兆迪, 朴世龙, 等. 1981~2000年中国陆地植被碳汇的估算[J]. 中国科学(D辑:地球科学), 2007(6): 804-812.

[26] 赵荣钦, 黄贤金. 基于能源消费的江苏省土地利用碳排放与碳足迹[J]. 地理研究, 2010, 29(9): 1639-1649.

[27] 周嘉, 王钰萱, 刘学荣, 等. 基于土地利用变化的中国省域碳排放时空差异及碳补偿研究[J]. 地理科学, 2019, 39(12): 1955-1961.

[28] 赖力. 中国土地利用的碳排放效应研究[D]. 南京: 南京大学, 2010.

[29] 赵雲泰, 黄贤金, 钟太洋, 等. 1999～2007年中国能源消费碳排放强度空间演变特征[J]. 环境科学, 2011, 32(11): 3145-3152.

[30] 孙赫, 梁红梅, 常学礼, 等. 中国土地利用碳排放及其空间关联[J]. 经济地理, 2015, 35(3): 154-162.

[31] 李缘缘, 魏伟, 周俊菊, 等. 中国土地利用碳排放变化及协调分区[J]. 环境科学, 2023, 44(3): 1267-1276

[32] 李建豹, 黄贤金, 孙树臣, 等. 长三角地区城市土地与能源消费CO2排放的时空耦合分析[J]. 地理研究, 2019, 38(9): 2188-2201.

[33] 魏燕茹, 陈松林. 福建省土地利用碳排放空间关联性与碳平衡分区[J]. 生态学报, 2021, 41(14): 5814-5824.

[34] 胡孟然. 中国县域碳排放空间格局及影响因素研究[D]. 兰州: 兰州大学, 2022.

[35] 黄汉志, 贾俊松, 张振旭. 江西县域土地利用变化碳排放时空演变及其影响因素[J]. 生态学报, 2023, 43(20): 8390-8403.

[36] 张杰, 陈海, 刘迪, 等. 基于县域尺度土地利用碳排放的时空分异及影响因素研究[J]. 西北大学学报(自然科学版), 2022, 52(1): 21-31.

[37] 向书江, 杨春梅, 谢雨琦, 等. 近20年重庆市主城区碳排放的时空动态演进及其重心迁移[J]. 环境科学, 2023, 44(1): 560-571.

[38] 王劲峰, 徐成东. 地理探测器: 原理与展望[J]. 地理学报, 2017, 72(1): 116-134.

[39] Ang B W. The LMDI approach to decomposition analysis: a practical guide[J]. Energy Policy, 2005, 33(7): 867-871.

[40] Tadhg O' Mahony. Decomposition of Ireland's carbon emissions from 1990 to 2010: An extended Kaya identity[J]. Energy Policy, 2013, 59: 573-581.

[41] 阿如旱, 牡丹, 苏德苏日古格, 等. 内蒙古多伦县土地利用碳排放及其影响因素分析[J]. 干旱区资源与环境, 2019, 33(4): 17-22.

[42] 曹周亮, 张欣蓉, 员学锋, 等. 基于土地利用变化的陕西省县域碳排放时空变化及影响因素研究[J]. 水土保持通报, 2022, 42(5): 376-385.

[43] 张圆圆, 陈方媛, 许翔, 等. 基于县域尺度的福建省土地利用碳排放效应分析[J]. 环境科学研究, 2023, 36(7): 1446-1456.

[44] 匡文慧, 张树文, 杜国明, 等. 2015—2020年中国土地利用变化遥感制图及时空特征分析[J]. 地理学报, 2022, 77(5): 1056-1071.

[45] 奥勇, 毋冰龙, 白召弟, 等. 基于类NPP-VIIRS夜间灯光数据的粤港澳大湾区城市建成区时空动态特征[J]. 地球科学与环境学报, 2022, 44(3): 513-523.

[46] 伍亿真, 施开放, 余柏蒗, 等. 利用NPP-VIIRS夜间灯光遥感数据分析城市蔓延对雾霾污染的影响[J]. 武汉大学学报(信息科学版), 2021, 46(5): 777-789.

[47] 胡为安, 刘传立, 詹淇雯. 中国区域NPP-VIIRS年度夜间灯光数据的合成方法与对比验证[J]. 桂林理工大学学报, 2021, 41(1): 141-148.

[48] 关靖云, 李东, 王亚菲, 等. 中国区域DMSP-OLS与NPP-VIIRS夜间灯光影像校正[J]. 测绘通报, 2021(9): 1-8.

[49] Chen X, Nordhaus W D. VIIRS Nighttime Lights in the Estimation of Cross-Sectional and Time-Series GDP[J]. Remote Sensing, 2019, 11(9): 1057.

[50] 刘石俊, 薛亮. 利用夜间灯光数据探究陕西经济发展时空变化[J]. 遥感信息, 2021, 36(3): 113-121.

[51] 余柏蒗, 王丛笑, 宫文康, 等. 夜间灯光遥感与城市问题研究: 数据、方法、应用和展望[J]. 遥感学报, 2021, 25(1): 342-364.

[52] 孙秀锋, 施开放, 吴健平. 县级尺度的重庆市碳排放时空格局动态[J]. 环境科学, 2018, 39(6): 2971-2981.

[53] Miller H J. Tobler’s First Law and Spatial Analysis[J]. Annals of Association of American Geographers, 2004, 94(2): 284-289.

[54] 牛亚文, 赵先超, 胡艺觉. 基于NPP-VIIRS夜间灯光的长株潭地区县域土地利用碳排放空间分异研究[J]. 环境科学学报, 2021, 41(9): 3847-3856.

[55] 杨雪璐. 山西省土地利用碳排放时空特征及影响因素研究[D]. 太原: 山西财经大学, 2024.

[56] 袁霄, 倪九派, 谢德体. 重庆市县域空间土地利用碳排放的时空格局分析[J]. 应用基础与工程科学学报, 2021, 29(1): 25-38.

[57] Gallo J L. Space-time analysis of GDP disparities among European regions: A Markov chains approach[J]. International Regional Science Review, 2004, 27(2): 138-163.

[58] Liao F H F, Wei Y D. Dynamics, space, and regional inequality in provincial China: A case study of Guangdong province[J]. Applied Geography, 2012, 35(1-2): 71-83.

[59] 王少剑, 黄永源. 中国城市碳排放强度的空间溢出效应及驱动因素[J]. 地理学报, 2019, 74(6): 1131-1148.

[60] 莫惠斌, 王少剑. 黄河流域县域碳排放的时空格局演变及空间效应机制[J]. 地理科学, 2021, 41(8): 1324-1335.

[61] 赵荣钦, 黄贤金, 钟太洋. 中国不同产业空间的碳排放强度与碳足迹分析[J]. 地理学报, 2010, 65(9): 1048-1057.

[62] 杨庆媛. 土地利用变化与碳循环[J]. 中国土地科学, 2010, 24(10): 7-12.

[63] Cheng Y Q, Wang Z Y, Ye X Y, et al. Spatiotemporal dynamics of carbon intensity from energy consumption in China[J]. Journal of Geographical Sciences, 2014, 24(4): 631-650.

[64] 张润森, 濮励杰, 文继群, 等. 建设用地扩张与碳排放效应的库兹涅茨曲线假说及验证[J]. 自然资源学报, 2012,27(5): 723-733.

[65] 孙贤斌. 安徽省会经济圈土地利用变化的碳排放效益[J]. 自然资源学报, 2012, 27(3): 394-401.

[66] 苏雅丽, 张艳芳. 陕西省土地利用变化的碳排放效益研究[J]. 水土保持学报, 2011, 25(1): 152-156.

[67] 王军伟. 陕西省土地利用碳排放效应及低碳优化研究[D]. 西安: 长安大学, 2023.

[68] 王涛, 冯杰. 陕西省土地利用碳排放演变与影响因素分析研究[J]. 环境科学与管理, 2016, 41(12): 25-29.

[69] 张杰, 陈海, 刘迪, 等. 基于县域尺度土地利用碳排放的时空分异及影响因素研究[J]. 西北大学学报(自然科学版), 2022, 52(1): 21-31.

[70] 彭文甫, 周介铭, 徐新良, 等. 基于土地利用变化的四川省碳排放与碳足迹效应及时空格局[J]. 生态学报, 2016, 36(22): 7244-7259.

[71] 赖力, 黄贤金, 刘伟良, 等. 基于投入产出技术的区域生态足迹调整分析—以2002年江苏为例. 生态学报, 2006, 26(4): 1285-1292．

[72] 段晓男, 王效科, 逯非, 等. 中国湿地生态系统固碳现状和潜力[J]. 生态学报, 2008(2): 463-469.

[73] 石洪昕, 穆兴民, 张应龙, 等. 四川省广元市不同土地利用类型的碳排放效应研究[J]. 水土保持通报, 2012, 32(3): 101-106.

[74] 冯颖, 马桂英. 陕西省土地利用碳排放变化及预测研究[J]. 环境保护与循环经济, 2023, 43(9): 34-39+56.

[75] 刘竹, 耿涌, 薛冰, 等. 城市能源消费碳排放核算方法[J]. 资源科学, 2011, 33(7): 1325-1330.

[76] 王文超. 中国省区能源消费与二氧化碳排放驱动因素分析及预测研究[D]. 大连: 大连理工大学, 2013.

[77] 苏泳娴, 陈修治, 叶玉瑶, 等. 基于夜间灯光数据的中国能源消费碳排放特征及机理[J]. 地理学报, 2013, 68(11): 1513-1526.

[78] 张慧琳. 基于DMSP/OLS夜间灯光数据的中国能源消费碳排放时空变化特征及驱动力研究[D]. 兰州: 兰州大学, 2019.

[79] 武娜, 沈镭, 钟帅. 基于夜间灯光数据的晋陕蒙能源消费碳排放时空格局[J]. 地球信息科学学报, 2019, 21(7): 1040-1050.

[80] 施开放. 多尺度视角下的中国碳排放时空格局动态及影响因素研究[D]. 上海: 华东师范大学, 2018.

[81] 武爱彬, 赵艳霞, 郭小平, 等. 碳中和目标下河北省土地利用碳排放格局演变与多情景模拟[J]. 农业工程学报, 2023, 39(14): 261-270+293.

[82] 郭安宁, 牛陆, 刘霈珈, 等. 黄河流域城市群土地利用的碳排放效应[J]. 经济地理, 2023, 43(9): 172-178+240.

[83] 马远, 刘真真. 黄河流域土地利用碳排放的时空演变及影响因素研究[J]. 生态经济, 2021, 37(7): 35-43.

[84] 秦琳. 江苏省土地利用碳排放影响因素及其预测研究[D]. 徐州: 中国矿业大学, 2023.

[85] 庄大方, 刘纪远. 中国土地利用程度的区域分异模型研究[J]. 自然资源学报, 1997(2): 10-16.