| 京津冀城市群建设用地扩张多情景模拟及其对生态系统碳储量的影响 |
| 摘要点击 368 全文点击 57 投稿时间：2023-05-25 修订日期：2023-08-07 |
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| 中文关键词 碳储量 建设用地扩张 InVEST模型 PLUS模型 京津冀城市群 |
| 英文关键词 carbon storage construction land expansion InVEST model PLUS model Beijing-Tianjin-Hebei Urban Agglomeration |
| 作者 | 单位 | E-mail | | 武爱彬 | 北京林业大学水土保持学院, 北京 100083
河北省科学院地理科学研究所, 河北省地理信息开发应用工程技术研究中心, 石家庄 050011 | wu.ai.bin@163.com | | 陈辅国 | 河北地质大学土地科学与空间规划学院, 石家庄 050031 | | | 赵艳霞 | 河北省科学院地理科学研究所, 河北省地理信息开发应用工程技术研究中心, 石家庄 050011 | | | 秦彦杰 | 河北省科学院地理科学研究所, 河北省地理信息开发应用工程技术研究中心, 石家庄 050011 | | | 刘欣 | 河北省科学院地理科学研究所, 河北省地理信息开发应用工程技术研究中心, 石家庄 050011 | | | 郭小平 | 北京林业大学水土保持学院, 北京 100083 | guoxp@bjfu.edu.cn |
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| 中文摘要 |
| 量化分析建设用地扩张对陆地生态系统碳储量的影响,探索模拟建设用地扩张的优化方案以提高未来生态系统碳储量,对区域可持续发展和生态文明建设具有重要的现实意义.基于土地利用和地理空间数据,利用生态系统服务与权衡综合评估（InVEST）模型和基于栅格的斑块生成土地利用模拟（PLUS）模型,以2 km网格为基本单元,核算分析了京津冀城市群2000~2020年生态系统碳储量和格局演变,拟合回归了建设用地变化和碳储量变化两者关系,设置不同城市扩张发展情景对京津冀城市群2030年土地利用格局进行了模拟并分析了不同发展情景下2020~2030年建设用地扩张对碳储量的影响.结果表明：①京津冀城市群2000、2010和2020年生态系统碳储量（以C计）分别为2 088.02、2 106.78和2 121.25 Tg,其中林地碳库占比最大.研究期间碳储量减少的网格单元集中分布在北京、天津、石家庄和唐山等大城市周边,建设用地扩张的区域是碳储量变化最为剧烈的区域.②在建设用地占比10%以上的各等级网格单元区域,建设用地扩张和碳储量变化回归拟合关系良好,两者回归系数均呈现波动上升趋势.③在自然发展、建设用地扩张增速减少15%和建设用地扩张增速减少30%这3种发展情景下,2030年生态系统碳储量（以C计）分别为2 129.12、2 133.55和2 139.10 Tg.2020~2030年建设用地扩张和碳储量变化两者回归拟合效果均明显优于2000~2010年和2010~2020年,回归系数随着建设用地占比等级的增加均呈现波动增加的趋势.在各建设用地占比等级区域,回归系数值均呈现：自然发展情景<建设用地扩张增速减少15%发展情景<建设用地扩张增速减少30%发展情景.在“双碳”目标下,京津冀城市群应优先选择建设用地扩张增速降低发展情景,对建设用地的扩张应优先控制在建设用地占比较高的区域. |
| 英文摘要 |
| It is of great practical significance for regional sustainable development and ecological construction to quantitatively analyze the impact of construction land expansion on terrestrial ecosystem carbon storage and to explore the optimization scheme of simulating construction land expansion to improve future ecosystem carbon storage. Based on the land use and cover change (LUCC) and other geospatial data of the Beijing-Tianjin-Hebei Urban Agglomeration from 2000 to 2020, this study utilized the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model and the patch-generating land-use simulation (PLUS) model to assess and analyze the changes in ecosystem carbon stocks and spatial patterns regionally. In this study, we performed linear regression analysis to investigate the relationship between urban land expansion and changes in ecosystem carbon stocks for varying urban land proportion levels during two distinct time intervals, 2000-2010 and 2010-2020, which was conducted at a spatial resolution of 2 km. Three distinct urban land expansion scenarios were subjected to simulation to forecast the prospective land use pattern by 2030. Subsequently, we quantified the ramifications of these scenarios on ecosystem carbon stocks during the period from 2020 to 2030. The results were as follows:① In the Beijing-Tianjin-Hebei Urban Agglomeration, the ecosystem carbon stocks exhibited notable variations over the study period, with values of 2 088.02, 2 106.78, and 2 121.25 Tg recorded for the years 2000, 2010, and 2020, respectively, resulting in a cumulative carbon sequestration of 33.23 Tg C during the study duration. It is noteworthy that forest carbon storage emerged as the dominant contributor, with an increase from 1 010.17 Tg in 2000 to 1 136.53 Tg in 2020. Throughout the study period, the spatial distribution of carbon stocks displayed relative stability. Regions characterized by lower carbon content were concentrated in the vicinity of the Bohai Rim region and in proximity to cities such as Beijing, Tianjin, and Shijiazhuang, as well as rural settlements. In contrast, grid units with moderate and high carbon stocks were predominantly situated in the western Taihang Mountain and the northern Yanshan Mountain. Additionally, there was a tendency of increasing carbon stocks in the Taihang Mountain and Yanshan Mountain region, whereas those surrounding major urban centers such as Beijing, Tianjin, Shijiazhuang, and Tangshan experienced a notable decline in carbon stocks. Such reductions were most pronounced in regions undergoing urban land expansion during the study period. ② In grid units with an urban land proportion exceeding 10% at each level, a strong correlation was observed between urban land expansion and changes in carbon stocks during both the 2000-2010 and 2010-2020 periods. The changes in urban land proportion adequately explained the variations in carbon stocks. However, the explanatory power of urban land on carbon stocks decreased during the 2010-2020 period, indicating that other factors played a more substantial role in influencing carbon stocks during this time. The regression coefficients for both periods exhibited a fluctuating upward trend. In comparison to that during the 2000-2010 period, the impact of urban land expansion on carbon stocks was relatively smaller during 2010-2020, indicating a weakening influence. ③ In light of three distinct development scenarios, namely natural development (Scenario Ⅰ), a 15% reduction in the rate of urban land expansion (Scenario Ⅱ), and a 30% reduction in the rate of urban land expansion (Scenario Ⅲ), the projected ecosystem carbon stocks for the Beijing-Tianjin-Hebei Urban Agglomeration in the year 2030 were estimated to be 2 129.12, 2 133.55, and 2 139.10 Tg, respectively. These projections indicated an increase of 7.88, 12.30, and 17.85 Tg in comparison to the current carbon stocks. All scenarios demonstrated that the terrestrial ecosystem would play a role of carbon sink, particularly with the greatest carbon sink observed in the scenario with a 30% reduction in urban land expansion. The fit performance between urban land expansion and carbon stock changes during the 2020-2030 period was significantly better than that during the 2000-2010 and 2010-2020 periods, and the regression coefficients showed a fluctuating increase with an increase in urban land proportion. Across grid units with different urban land proportion levels, the regression coefficients exhibited the order of Scenario Ⅰ < Scenario Ⅱ < Scenario Ⅲ. In pursuit of the carbon peaking and carbon neutrality goals, the Beijing-Tianjin-Hebei Urban Agglomeration should prioritize scenarios with reduced rates of urban land expansion, especially in regions with higher urban land proportions. |
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