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学者姓名：张兰怡
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Abstract ：
Road construction profoundly affects forest landscapes, but its impact on the coupling between forest resilience and fragmentation remains underexplored. Thus, we calculated kernel density estimation (KDE) as an indicator of road density and quantified spatiotemporal dynamics of forest resilience and fragmentation in China from 2010 to 2020. We further examined how road density influences the coupling between forest resilience and fragmentation. Results showed that: (1) Forest resilience increased nationally, and fragmentation declined in most regions. (2) Bivariate spatial autocorrelation between coupling dynamics and KDE revealed high-high clusters in the Yangtze and Pearl River Deltas, and low-low in Daxinganling and southwestern Tibet. (3) SHapley Additive exPlanations showed that high KDE contributed positively and low KDE contributed negatively to coupling dynamics; multiscale geographically weighted regression revealed stronger synergistic effects in the pan-south and weaker in the northeast. This study offers quantitative insights into sustainable road planning and supports ecological restoration strategies.
Keyword ：
eXtreme Gradient Boosting (XGBoost) eXtreme Gradient Boosting (XGBoost) Forest fragmentation Forest fragmentation Forest resilience Forest resilience Machine learning Machine learning (MGWR) (MGWR) Multiscale geographically weighted regression Multiscale geographically weighted regression Road density Road density
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| GB/T 7714 | Qiu, Weiguo , Liu, Miaomiao , Fu, Qingjie et al. Impact of road density on the coupling of forest resilience and fragmentation [J]. | TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT , 2026 , 150 . |
| MLA | Qiu, Weiguo et al. "Impact of road density on the coupling of forest resilience and fragmentation" . | TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT 150 (2026) . |
| APA | Qiu, Weiguo , Liu, Miaomiao , Fu, Qingjie , Wang, Zhanyong , Zhang, Lanyi , Jiang, Li et al. Impact of road density on the coupling of forest resilience and fragmentation . | TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT , 2026 , 150 . |
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随着道路交通碳排放量的不断攀升，全球温室效应不断加剧。近年来，在工业、能源等领域，结合因素分解模型与碳排放预测模型的双模型方法已显示出其在揭示碳排放关键驱动因素和探明碳达峰路径方面的优势，但在道路交通领域的应用尚显不足。本研究利用多尺度排放清单模型获取2001年至2019年中国道路交通碳排放数据，并采用GDIM方法对影响碳排放的驱动因素（包括GDP、道路交通能源消耗量、单位能耗碳排放量、人口总量、道路交通人均碳排放量、人均GDP、单位GDP能耗和道路交通碳排放强度）进行分解。其次，设计五种逐层递进的情景，以评估不同政策组合下的减排潜力；最后，运用LEAP模型对2021—2035年中国道路交通的碳达峰情况进行情景仿真和预测。结果显示：（1）在各驱动因素中，GDP是影响交通碳排放的最主要因素，而人均GDP是抑制碳排放的关键；（2）在各情景模拟中，中经济发展强效低碳情景（SLSC）和中经济发展强化低碳情景（ELSC）展现出最佳的减排效果，预计在2024年均能实现碳达峰，其峰值碳排放量分别为1399.9Mt和1402.69Mt；在所有车型中，商用车碳排放将于2020年的744Mt增长至2035年的约800—1300Mt，相较于其他车型，其碳减排潜力巨大；（3）尽管摩托车的碳排放量在三种车型中最低，但其排放量呈上升趋势。摩托车在单独实行“摩改电”措施后无法实现碳达峰，需要未来进一步的管控措施配合其他政策同步实施才能实现碳达峰。本研究所提出的模型及方法在交通运输碳减排中具有较好的参考价值。
Keyword ：
情景分析 情景分析 碳达峰 碳达峰 道路交通 道路交通 长期能源替代规划系统(LEAP)模型 长期能源替代规划系统(LEAP)模型 驱动因素分解 驱动因素分解
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| GB/T 7714 | 王硕 , 徐艺诺 , 翁大维 et al. 中国道路交通碳排放驱动因素及达峰分析 [J]. | 生态学报 , 2025 , (03) . |
| MLA | 王硕 et al. "中国道路交通碳排放驱动因素及达峰分析" . | 生态学报 03 (2025) . |
| APA | 王硕 , 徐艺诺 , 翁大维 , 张煌帆 , 温晓娟 , 胡喜生 et al. 中国道路交通碳排放驱动因素及达峰分析 . | 生态学报 , 2025 , (03) . |
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The rapid urban sprawl and economic prosperity in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) have propelled road transportation to become a prominent contributor to regional carbon emissions. Road traportation-related carbon emissions are primarily generated by the large number of motor vehicles in operation. In this research, a bottom-up methodology was adopted to assess road transportation-related carbon emissions in key urban agglomeration within the GBA region (KUA-GBA). The Logarithmic Mean Divisia Index (LMDI) model was applied to break down these emissions into four main components: energy efficiency, economic growth, population scale, and intensity of energy consumption. Furthermore, this study utilized the Longrange Energy Alternatives Planning (LEAP) model along with pertinent policy scenarios to project and evaluate the potential reduction in road transportation carbon emissions in the KUA-GBA region from 2023 to 2035. The findings indicate that: firstly, regarding the contribution rate of carbon emissions by vehicle type, SGPVs, HDFVs, and LGFVs are the primary contributors to transportation-related carbon emissions in the KUA-GBA region, accounting for 48.7 %, 24.2 %, and 13.9 % of total vehicle carbon emissions, respectively. Secondly, from the perspective of driving factors for carbon emissions, the energy intensity effect is the most significant driver of transportation-related carbon emissions. Between 2018 and 2022, this factor alone contributed over 1907 kt of carbon emissions. Finally, in terms of carbon emission reduction pathways, improving vehicle fuel economy holds the greatest potential for emission reductions, with a projected carbon reduction contribution rate of approximately 68 % by 2035. Additionally, combining measures such as optimizing vehicle structure, enhancing vehicle energy efficiency, and reducing annual mileage can achieve optimal carbon emission reductions. The findings from this analysis can offer more precise recommendations for reducing carbon emissions in the transportation sectors of the KUA-GBA region, thus aiding in the earlier attainment of the dual-carbon objectives.
Keyword ：
Carbon emissions Carbon emissions Emission trends Emission trends Road transportation Road transportation Scenario simulation Scenario simulation Spatiotemporal characteristics Spatiotemporal characteristics
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| GB/T 7714 | Xu, Yinuo , Zhang, Huangfan , Weng, Dawei et al. An integrated assessment of road transportation carbon emissions in a rapidly urbanizing megaregion: Evidence from the KUA-GBA, China [J]. | JOURNAL OF CLEANER PRODUCTION , 2025 , 525 . |
| MLA | Xu, Yinuo et al. "An integrated assessment of road transportation carbon emissions in a rapidly urbanizing megaregion: Evidence from the KUA-GBA, China" . | JOURNAL OF CLEANER PRODUCTION 525 (2025) . |
| APA | Xu, Yinuo , Zhang, Huangfan , Weng, Dawei , Wen, Xiaojuan , Wang, Shuo , Xie, Zhengyi et al. An integrated assessment of road transportation carbon emissions in a rapidly urbanizing megaregion: Evidence from the KUA-GBA, China . | JOURNAL OF CLEANER PRODUCTION , 2025 , 525 . |
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Forest ecosystems are essential for mitigating global environmental change. However, the impact of the rapid global urbanization process on forest carbon sequestration remains unclear. Hence, we calculated the foresturban carbon balance index (CBI) from 1985 to 2020 at multiple scales (continental, national, and climatic). We applied eXtreme Gradient Boosting (XGBoost) combined with SHapley Additive exPlanations (SHAP) to identify key driving factors across regions and used multiscale geographically weighted regression (MGWR) to uncover spatial inequalities in their effects on carbon balance. Results revealed that: (1) CBI increased from 0.77 to 3.02 g C/m2/y- 1 during 1985-2020, while forest carbon flux decreased from -3.55 to -7.68 g C/m2/y- 1. Urbanization carbon loss was 1.90 times that of forest carbon loss, with high values concentrated in eastern China and northern India. (2) CBI hotspots were identified in the Amazon, the Congo Basin, and Southeast Asia, while cold spots were found in the boreal forests of Canada and Russia. (3) XGBoost-SHAP analysis revealed that temperature and precipitation were key influences in regions such as the Amazon and Sub-Saharan Africa, whereas population density was a dominant factor in the southwestern United States and eastern Australia. (4) MGWR analysis identified slope, temperature, precipitation, road density, and human footprint as synergistic drivers, while elevation, accessibility to cities, and GDP had trade-off effects. These drivers displayed spatial heterogeneity, with varying bandwidth impacts. Our findings enhance understanding of global forest-urban carbon dynamics and highlight regional development inequalities, supporting efforts in reforestation and urban planning for sustainable carbon management.
Keyword ：
Forest carbon loss Forest carbon loss Forest carbon sequestration Forest carbon sequestration (MGWR) (MGWR) Multiscale geographically weighted regression Multiscale geographically weighted regression SHapley additive exPlanations (SHAP) SHapley additive exPlanations (SHAP) Urbanization carbon loss Urbanization carbon loss
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| GB/T 7714 | Qiu, Weiguo , Guo, Rongpeng , Jia, Dingyi et al. Spatio-temporal dynamics of global forest-urban carbon balance and its driving patterns during 1985-2020 [J]. | JOURNAL OF CLEANER PRODUCTION , 2025 , 519 . |
| MLA | Qiu, Weiguo et al. "Spatio-temporal dynamics of global forest-urban carbon balance and its driving patterns during 1985-2020" . | JOURNAL OF CLEANER PRODUCTION 519 (2025) . |
| APA | Qiu, Weiguo , Guo, Rongpeng , Jia, Dingyi , Wu, Min , Wang, Zhanyong , Zhang, Lanyi et al. Spatio-temporal dynamics of global forest-urban carbon balance and its driving patterns during 1985-2020 . | JOURNAL OF CLEANER PRODUCTION , 2025 , 519 . |
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随着道路交通碳排放量的不断攀升，全球温室效应不断加剧。近年来，在工业、能源等领域，结合因素分解模型与碳排放预测模型的双模型方法已显示出其在揭示碳排放关键驱动因素和探明碳达峰路径方面的优势，但在道路交通领域的应用尚显不足。利用多尺度排放清单模型获取2001年至2019年中国道路交通碳排放数据，并采用GDIM方法对影响碳排放的驱动因素(包括GDP、道路交通能源消耗量、单位能耗碳排放量、人口总量、道路交通人均碳排放量、人均GDP、单位GDP能耗和道路交通碳排放强度)进行分解。其次，设计五种逐层递进的情景，以评估不同政策组合下的减排潜力；最后，运用LEAP模型对2021—2035年中国道路交通的碳达峰情况进行情景仿真和预测。结果显示：(1)在各驱动因素中，GDP是影响交通碳排放的最主要因素，而人均GDP是抑制碳排放的关键；(2)在各情景模拟中，中经济发展强效低碳情景(SLSC)和中经济发展强化低碳情景(ELSC)展现出最佳的减排效果，预计在2024年均能实现碳达峰，其峰值碳排放量分别为1399.9Mt和1402.69Mt;在所有车型中，商用车碳排放将于2020年的744Mt增长至2035年的约800—1300Mt,相较于其他车型，其碳减排潜力巨大；(3)尽管摩托车的碳排放量在三种车型中最低，但其排放量呈上升趋势。摩托车在单独实行“摩改电”措施后无法实现碳达峰，需要未来进一步的管控措施配合其他政策同步实施才能实现碳达峰。本研究所提出的模型及方法在交通运输碳减排中具有较好的参考价值。
Keyword ：
情景分析 情景分析 碳达峰 碳达峰 道路交通 道路交通 长期能源替代规划系统(LEAP)模型 长期能源替代规划系统(LEAP)模型 驱动因素分解 驱动因素分解
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| GB/T 7714 | 王硕 , 徐艺诺 , 翁大维 et al. 中国道路交通碳排放驱动因素及碳达峰 [J]. | 生态学报 , 2025 , 45 (03) : 1315-1327 . |
| MLA | 王硕 et al. "中国道路交通碳排放驱动因素及碳达峰" . | 生态学报 45 . 03 (2025) : 1315-1327 . |
| APA | 王硕 , 徐艺诺 , 翁大维 , 张煌帆 , 温晓娟 , 胡喜生 et al. 中国道路交通碳排放驱动因素及碳达峰 . | 生态学报 , 2025 , 45 (03) , 1315-1327 . |
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Achieving carbon peak in the transportation sector is critical for mitigating climate change and promoting sustainable urban development. This study focuses on the Urban Agglomeration on the West Coast of the China Strait (UAWCC), a major economic region with high transportation demand and significant energy consumption. Using the Logarithmic Mean Divisia Index (LMDI) method, we quantify the key drivers of transportation-related carbon emissions, including energy structure, energy consumption, transport intensity, economic development, and population dynamics. We further apply the Long-range Energy Alternatives Planning (LEAP) model to simulate energy consumption and emissions from 2020 to 2035 under baseline, low-carbon, and enhanced lowcarbon scenarios. The results indicate that without intervention, emissions will continue to rise, whereas under stringent low-carbon policies, emissions could peak by 2030. The analysis also incorporates methane (CH4), nitrous oxide (N2O), volatile organic compounds (VOCs), and sulfur dioxide (SO2), providing a comprehensive assessment of co-benefits for air quality. The research offers valuable insights for policymakers in advancing clean energy adoption, optimizing transport structures, and formulating targeted strategies to support China's carbon peaking goals.
Keyword ：
Carbon peak Carbon peak Emission mitigation Emission mitigation LEAP model LEAP model LMDI decomposition LMDI decomposition Low-carbon transportation Low-carbon transportation
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| GB/T 7714 | Zhang, Lanyi , Weng, Dawei , Wen, Xiaojuan et al. Pathways towards carbon-peak transportation in China: Energy alternatives and emission mitigation strategies [J]. | ENERGY FOR SUSTAINABLE DEVELOPMENT , 2025 , 88 . |
| MLA | Zhang, Lanyi et al. "Pathways towards carbon-peak transportation in China: Energy alternatives and emission mitigation strategies" . | ENERGY FOR SUSTAINABLE DEVELOPMENT 88 (2025) . |
| APA | Zhang, Lanyi , Weng, Dawei , Wen, Xiaojuan , Xie, Zhengyi , Ke, Ting , Hu, Xisheng . Pathways towards carbon-peak transportation in China: Energy alternatives and emission mitigation strategies . | ENERGY FOR SUSTAINABLE DEVELOPMENT , 2025 , 88 . |
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本发明公开了一种基于最小道路阻抗模型的低碳道路选线方法，包括：获取研究区的高程栅格数据图层；获取中国土地碳汇栅格数据图层，通过裁剪得出研究区碳汇栅格数据图层；将所述高程栅格数据图层进行标准化，得到地表高程阻力面；将所述研究区碳汇栅格数据图层进行标准化，得到土地碳汇阻力面；将所述地表高程阻力面对应的阻力值与土地碳汇阻力面对应的阻力值进行加权叠加，得到研究区的成本栅格数据；在研究区的公路交通地图上确定规划路线的起点与终点；根据成本栅格数据、起点的坐标信息与终点的坐标信息对起点与终点之间的成本距离和成本路径进行分析，得到起点与终点之间的最低成本路径。本发明为解决人工选线方法存在的问题提供科学依据。
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| GB/T 7714 | 林程翔 , 胡喜生 , 巫志龙 et al. 一种基于最小道路阻抗模型的低碳道路选线方法 : CN202311679211.8[P]. | 2023-12-07 . |
| MLA | 林程翔 et al. "一种基于最小道路阻抗模型的低碳道路选线方法" : CN202311679211.8. | 2023-12-07 . |
| APA | 林程翔 , 胡喜生 , 巫志龙 , 林森 , 王占永 , 张兰怡 . 一种基于最小道路阻抗模型的低碳道路选线方法 : CN202311679211.8. | 2023-12-07 . |
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The expansion of road networks profoundly affects ecological systems by intensifying habitat fragmentation, altering hydrological processes, and exacerbating pollution. However, our understanding of the multi-scale spatiotemporal coupling between road networks and ecological quality remains limited. Thus, taking Fuzhou City in Southeastern China as a case study (similar to 12,000 km(2)), we apply bivariate spatial autocorrelation, geographical detectors (GDs), and multi-scale geographically weighted regression (MGWR) to explore the multi-scale interactions between road networks and ecological quality. Results reveal the following: (1) From 2016 to 2021, kernel density estimation (KDE) analysis of the road network indicates coordinated growth in both urban and rural areas, with an increase of 0.759 km/km(2). Analysis based on the remote sensing-based ecological index (RSEI) shows a decrease from 2000 to 2016, and then an increase from 2016 to 2021, with a trend of increasing gradually from urban center to rural area. (2) Predominant tradeoff relationships exist between KDE and RSEI in 2016 and 2021, while notable synergistic relationships emerge between Delta KDE and Delta RSEI. (3) Multi-scale GD analysis identifies Delta KDE as a principal factor influencing Delta RSEI, and the MGWR reveals their significant synergistic associations at an optimal scale of 3000 m. These findings highlight the unequal impact of road network expansion on ecological quality, underscoring the pivotal role of road density changes in its spatiotemporal dynamics. They offer essential insights for sustainable transport and ecological planning.
Keyword ：
bivariate spatial autocorrelation bivariate spatial autocorrelation geographical detectors (GDs) geographical detectors (GDs) multi-scale geographically weighted regression (MGWR) multi-scale geographically weighted regression (MGWR) remote sensing-based ecological index (RSEI) remote sensing-based ecological index (RSEI) road network road network
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| GB/T 7714 | Qiu, Weiguo , Jia, Dingyi , Guo, Rongpeng et al. Unequal Impact of Road Expansion on Regional Ecological Quality [J]. | LAND , 2025 , 14 (3) . |
| MLA | Qiu, Weiguo et al. "Unequal Impact of Road Expansion on Regional Ecological Quality" . | LAND 14 . 3 (2025) . |
| APA | Qiu, Weiguo , Jia, Dingyi , Guo, Rongpeng , Zhang, Lanyi , Wang, Zhanyong , Hu, Xisheng . Unequal Impact of Road Expansion on Regional Ecological Quality . | LAND , 2025 , 14 (3) . |
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赣闽粤地区紧邻东南沿海地区，是珠三角、长三角和粤闽浙等地区的腹地.随着赣闽粤地区经济发展和城市化进程加快，交通所带来的高碳排放问题日益突出.我国“十四五”规划提出深入实施区域协调发展战略，健全区域协调发展机制.长期以来，国内外学术研究和政策关注往往集中在经济发达区域，如长三角、珠三角和京津冀地区.以上区域的经济发展模式和机制已经得到了充分的研究和验证.然而，赣闽粤地区则展示出经济发展不均衡的特征，既包含经济发达的珠三角地区（广东省），也包含相对欠发达的内陆区域（江西省），故对于赣闽粤地区的研究有助于全面理解区域经济发展的多样性和不均衡性，并为其他欠发达地区提供参考.综上，针对赣闽粤地区交通碳排放时空差异及驱动力尚不明确的挑战，引入标准差椭圆分析法探明赣闽粤地区交通碳排放的空间分布特征（2009～2021年），基于所构建的LMDI和M-R分解模型从时间和空间层面探明碳排放空间分异的驱动力.结果表明：(1)赣闽粤地区交通碳排放总量呈逐年增加趋势，其碳排放重心主要位于广东省和福建省的交界处，且持续向“东北-西南”方向转变；(2)经济发展水平和人口规模是交通碳排放的主要驱动力；而能源强度是关键的抑制因素；(3)三省碳排放与平均水平相比存在显著的空间差异.研究结论可以为制定赣闽粤地区差异化碳减排政策和联合治理措施提供重要参考.
Keyword ：
LMDI模型 LMDI模型 M-R模型 M-R模型 交通碳排放 交通碳排放 赣闽粤地区 赣闽粤地区 驱动力 驱动力
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| GB/T 7714 | 张兰怡 , 翁大维 , 王硕 et al. 赣闽粤地区交通碳排放时空演化及驱动力 [J]. | 环境科学 , 2025 , 46 (05) : 2886-2896 . |
| MLA | 张兰怡 et al. "赣闽粤地区交通碳排放时空演化及驱动力" . | 环境科学 46 . 05 (2025) : 2886-2896 . |
| APA | 张兰怡 , 翁大维 , 王硕 , 徐艺诺 , 罗翠云 , 胡喜生 et al. 赣闽粤地区交通碳排放时空演化及驱动力 . | 环境科学 , 2025 , 46 (05) , 2886-2896 . |
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This study addresses the gap in understanding carbon emissions from transportation across intraprovincial cities in Fujian Province, China's first ecological civilization pilot province. This research employs a top-down approach to estimate transportation carbon emissions in Fujian Province, complemented by Standard Deviation Ellipse (SDE) and Logarithmic Mean Divisia Index (LMDI) methods to analyze spatiotemporal variations and driving factors of emissions. We also incorporate a Multi-Region (M-R) decomposition model to assess the impact of economic, demographic, and land-use factors on emissions. The results reveal significant disparities among prefecture-level cities. The economic factor, land use structure, and population growth were identified as the main drivers of transportation carbon emissions, while land use output showed a significant mitigating effect, highlighting the role of improving land-use efficiency in reducing emissions. Coastal cities showed stronger emission-driving effects compared to inland areas. The findings offer targeted insights for low-carbon transportation planning and differentiated policymaking among cities.
Keyword ：
Carbon emissions Carbon emissions China China LMDI decomposition model LMDI decomposition model Standard Deviation Ellipse Standard Deviation Ellipse Top-down method Top-down method
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| GB/T 7714 | Zhang, Lanyi , Weng, Dawei , Wen, Xiaojuan et al. Unveiling intra-provincial cities carbon gap: Exploring transportation emissions in ecological Vanguard [J]. | TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT , 2025 , 144 . |
| MLA | Zhang, Lanyi et al. "Unveiling intra-provincial cities carbon gap: Exploring transportation emissions in ecological Vanguard" . | TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT 144 (2025) . |
| APA | Zhang, Lanyi , Weng, Dawei , Wen, Xiaojuan , Xu, Yinuo , Pan, Guiping , Zhang, Huangfan et al. Unveiling intra-provincial cities carbon gap: Exploring transportation emissions in ecological Vanguard . | TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT , 2025 , 144 . |
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