| 研究生: |
劉昱汝 Liou, Yu-Ru |
|---|---|
| 論文名稱: |
探討都會區未來土地利用變遷對電力碳排放之影響-以臺北都會區為例 Exploring the Impact of Future Land Use Change on Electricity Carbon Emissions in Metropolitan Areas: A Case Study of the Taipei Metropolitan Area |
| 指導教授: |
顧嘉安
Ku, Chia-An |
| 學位類別: |
碩士 Master |
| 系所名稱: |
規劃與設計學院 - 都市計劃學系 Department of Urban Planning |
| 論文出版年: | 2025 |
| 畢業學年度: | 113 |
| 語文別: | 中文 |
| 論文頁數: | 151 |
| 中文關鍵詞: | 土地利用變遷 、電力碳排放 、空間自相關 、淨零排放 、臺北都會區 |
| 外文關鍵詞: | Land use change, Electricity-related carbon emissions, Spatial autocorrelation, Net-zero emissions, Taipei Metropolitan Area |
| 相關次數: | 點閱:25 下載:0 |
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面對氣候變遷議題日益嚴峻,土地利用變遷與碳排放議題已成為不可忽視的議題。本研究以臺北都會區為實證範圍,探討未來土地利用變遷對電力碳排放之影響,以土地利用變遷模擬、碳排放係數法與空間自相關方法,討論不同發展情境下之碳排放變化趨勢與空間分布特性,並進一步分析碳排熱點與都市設施之空間關聯,作為未來低碳都市規劃之依據。
研究結果顯示,建成環境之擴張為未來都市發展與碳排放增加之主要驅動力,在都市核心碳排放具有明顯的空間聚集特性,集中於人口密度高、開發強度大與交通節點集中的區域;此外,在永續發展情境(SD)下,透過嚴格限制開發與強化綠地保存,可有效抑制碳排增長趨勢,展現出較低的總碳排量與較分散的熱區分布。
本研究進一步提出五項規劃策略建議,以碳效益為核心的土地利用原則、將碳排納入都市規劃實務操作、維持都市邊界與綠地保育、導入碳排動態情境模擬機制,以及推動區域治理與跨域協作機制。整體來說,本研究強調都市空間規劃與碳排放管理整合,提供空間數據與碳排模型之實證分析架構,期能作為未來都市氣候調適與淨零轉型規劃的重要參考。
In response to the growing challenges of climate change, this study examines the impact of future land use change on electricity-related carbon emissions in the Taipei Metropolitan Area. Using land use simulation, emission coefficient methods, and spatial autocorrelation analysis, it explores emission trends and spatial patterns under different development scenarios. The results reveal that the expansion of built-up areas is the main driver of carbon emissions, especially in high-density urban cores. Under the Sustainable Development (SD) scenario, stricter development controls and green space preservation effectively curb emissions and disperse hot spots. The study further proposes five planning strategies: carbon-efficient land use, integrating carbon management into urban planning, protecting urban boundaries and green spaces, adopting dynamic simulation mechanisms, and enhancing regional governance. This research provides a spatial and modeling-based framework to support low-carbon urban planning and net-zero transitions.
英文文獻
1 Adolfsson Jörby, S. (2000). Local Agenda 21 in practice–A Swedish example. Sustainable Development, 8(4), 201-214.
2 Agreement, P. (2015). Paris agreement. report of the conference of the parties to the United Nations framework convention on climate change (21st session, 2015: Paris). Retrived December,
3 Ahmad, H., Abdallah, M., Jose, F., Elzain, H. E., Bhuyan, M. S., Shoemaker, D. J., & Selvam, S. (2023). Evaluation and mapping of predicted future land use changes using hybrid models in a coastal area. Ecological Informatics, 78, 102324. https://doi.org/https://doi.org/10.1016/j.ecoinf.2023.102324
4 Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical analysis, 27(2), 93-115.
5 Bagan, H., & Yamagata, Y. (2014). Land-cover change analysis in 50 global cities by using a combination of Landsat data and analysis of grid cells. Environmental Research Letters, 9(6), 064015.
6 Batty, M. (2007). Cities and complexity: understanding cities with cellular automata, agent-based models, and fractals. The MIT press.
7 Batty, M., & Xie, Y. (1994). From cells to cities. Environment and Planning B: Planning and Design, 21(7), S31-S48.
8 Bianchini, S., Solari, L., Del Soldato, M., Raspini, F., Montalti, R., Ciampalini, A., & Casagli, N. (2019). Ground subsidence susceptibility (GSS) mapping in Grosseto Plain (Tuscany, Italy) based on satellite InSAR data using frequency ratio and fuzzy logic. Remote Sensing, 11(17), 2015.
9 Bununu, Y. A. (2017). Integration of Markov chain analysis and similarity-weighted instance-based machine learning algorithm (SimWeight) to simulate urban expansion [Article]. International Journal of Urban Sciences, 21(2), 217-237. https://doi.org/10.1080/12265934.2017.1284607
10 Cai, M., Shi, Y., Ren, C., Yoshida, T., Yamagata, Y., Ding, C., & Zhou, N. (2021). The need for urban form data in spatial modeling of urban carbon emissions in China: A critical review. Journal of Cleaner Production, 319, 128792. https://doi.org/https://doi.org/10.1016/j.jclepro.2021.128792
11 Camacho Olmedo, M., Paegelow, M., & Mas, J.-F. (2013). Interest in intermediate soft-classified maps in land change model validation: suitability versus transition potential. International Journal of Geographical Information Science, 27(12), 2343-2361.
12 Canadell, J. G., Monteiro, P. M., Costa, M. H., Da Cunha, L. C., Cox, P. M., Eliseev, A. V., Henson, S., Ishii, M., Jaccard, S., & Koven, C. (2021). Global carbon and other biogeochemical cycles and feedbacks. IPCC AR6 WGI, final government distribution, chapter 5.
13 Carter, J. G., Cavan, G., Connelly, A., Guy, S., Handley, J., & Kazmierczak, A. (2015). Climate change and the city: Building capacity for urban adaptation. Progress in planning, 95, 1-66.
14 Chang, C.-T., Yang, C.-H., & Lin, T.-P. (2019). Carbon dioxide emissions evaluations and mitigations in the building and traffic sectors in Taichung metropolitan area, Taiwan. Journal of Cleaner Production, 230, 1241-1255. https://doi.org/https://doi.org/10.1016/j.jclepro.2019.05.006
15 Chang, X., Xing, Y., Wang, J., Yang, H., & Gong, W. (2022). Effects of land use and cover change (LUCC) on terrestrial carbon stocks in China between 2000 and 2018. Resources, Conservation and Recycling, 182, 106333. https://doi.org/https://doi.org/10.1016/j.resconrec.2022.106333
16 Clarke, K. C., & Johnson, J. M. (2020). Calibrating SLEUTH with big data: Projecting California's land use to 2100. Computers, Environment and Urban Systems, 83, 101525.
17 Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and psychological measurement, 20(1), 37-46.
18 Couclelis, H. (1997). From cellular automata to urban models: new principles for model development and implementation. Environment and Planning B: Planning and Design, 24(2), 165-174.
19 Croci, E., Lucchitta, B., & Molteni, T. (2021). Low carbon urban strategies: An investigation of 124 European cities. Urban Climate, 40, 101022. https://doi.org/https://doi.org/10.1016/j.uclim.2021.101022
20 Cui, Y., Li, L., Chen, L., Zhang, Y., Cheng, L., Zhou, X., & Yang, X. (2018). Land-use carbon emissions estimation for the Yangtze River Delta Urban Agglomeration using 1994–2016 Landsat image data. Remote Sensing, 10(9), 1334.
21 Dey, N. N., Al Rakib, A., Kafy, A.-A., & Raikwar, V. (2021). Geospatial modelling of changes in land use/land cover dynamics using Multi-layer Perceptron Markov chain model in Rajshahi City, Bangladesh. Environmental Challenges, 4, 100148.
22 Domingo, D., Palka, G., & Hersperger, A. M. (2021). Effect of zoning plans on urban land-use change: A multi-scenario simulation for supporting sustainable urban growth. Sustainable Cities and Society, 69, 102833. https://doi.org/https://doi.org/10.1016/j.scs.2021.102833
23 Eastman, J. R. (2024). Terrset Manual. (Clark University)
24 Falah, N., Karimi, A., & Harandi, A. T. (2020). Urban growth modeling using cellular automata model and AHP (case study: Qazvin city). Modeling Earth Systems and Environment, 6, 235-248.
25 Falahatkar, S., & Rezaei, F. (2020). Towards low carbon cities: Spatio-temporal dynamics of urban form and carbon dioxide emissions. Remote Sensing Applications: Society and Environment, 18, 100317.
26 Feng, H., Wang, S., Zou, B., Nie, Y., Ye, S., Ding, Y., & Zhu, S. (2023). Land use and cover change (LUCC) impacts on Earth’s eco-environments: Research progress and prospects. Advances in Space Research, 71(3), 1418-1435. https://doi.org/https://doi.org/10.1016/j.asr.2022.09.054
27 Feng, H., Zou, B., & Tang, Y. (2017). Scale- and Region-Dependence in Landscape-PM2.5 Correlation: Implications for Urban Planning. Remote Sensing, 9(9), 918. https://www.mdpi.com/2072-4292/9/9/918
28 Feng, Y., Chen, S., Tong, X., Lei, Z., Gao, C., & Wang, J. (2020). Modeling changes in China’s 2000–2030 carbon stock caused by land use change. Journal of Cleaner Production, 252, 119659.
29 Friedlingstein, P., O'sullivan, M., Jones, M. W., Andrew, R. M., Gregor, L., Hauck, J., Le Quéré, C., Luijkx, I. T., Olsen, A., & Peters, G. P. (2022). Global carbon budget 2022. Earth System Science Data, 14(11), 4811-4900.
30 Gaur, S., Mittal, A., Bandyopadhyay, A., Holman, I., & Singh, R. (2020). Spatio-temporal analysis of land use and land cover change: a systematic model inter-comparison driven by integrated modelling techniques. International Journal of Remote Sensing, 41(23), 9229-9255.
31 Gaur, S., & Singh, R. (2023). A comprehensive review on land use/land cover (LULC) change modeling for urban development: current status and future prospects. Sustainability, 15(2), 903.
32 Getis, A., & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical analysis, 24(3), 189-206.
33 Gharaibeh, A., Shaamala, A., Obeidat, R., & Al-Kofahi, S. (2020). Improving land-use change modeling by integrating ANN with Cellular Automata-Markov Chain model. Heliyon, 6(9).
34 Greiner, R., Puig, J., Huchery, C., Collier, N., & Garnett, S. T. (2014). Scenario modelling to support industry strategic planning and decision making. Environmental Modelling & Software, 55, 120-131.
35 Gui, D., He, H., Liu, C., & Han, S. (2023). Spatio-temporal dynamic evolution of carbon emissions from land use change in Guangdong Province, China, 2000–2020. Ecological Indicators, 156, 111131.
36 Güneralp, B., Zhou, Y., Ürge-Vorsatz, D., Gupta, M., Yu, S., Patel, P. L., Fragkias, M., Li, X., & Seto, K. C. (2017). Global scenarios of urban density and its impacts on building energy use through 2050. Proceedings of the National Academy of Sciences, 114(34), 8945-8950.
37 Hagenauer, J., & Helbich, M. (2012). Mining urban land-use patterns from volunteered geographic information by means of genetic algorithms and artificial neural networks. International Journal of Geographical Information Science, 26(6), 963-982.
38 Han, X., Yu, J., Xia, Y., & Wang, J. (2021). Spatiotemporal characteristics of carbon emissions in energy-enriched areas and the evolution of regional types. Energy Reports, 7, 7224-7237.
39 Hasan, S., Shi, W., Zhu, X., Abbas, S., & Khan, H. U. A. (2020). Future simulation of land use changes in rapidly urbanizing South China based on land change modeler and remote sensing data. Sustainability, 12(11), 4350.
40 He, J. J., & Zhang, P. Y. (2022). Evaluation of carbon emissions associated with land use and cover change in Zhengzhou City of China [Article]. Regional Sustainability, 3(1), 1-11. https://doi.org/10.1016/j.regsus.2022.03.002
41 Hong, T., Huang, X., Zhang, X., & Deng, X. (2023). Correlation modelling between land surface temperatures and urban carbon emissions using multi-source remote sensing data: A case study. Physics and Chemistry of the Earth, Parts A/B/C, 132, 103489. https://doi.org/https://doi.org/10.1016/j.pce.2023.103489
42 Houghton, R. A., House, J. I., Pongratz, J., Van Der Werf, G. R., Defries, R. S., Hansen, M. C., Le Quéré, C., & Ramankutty, N. (2012). Carbon emissions from land use and land-cover change. Biogeosciences, 9(12), 5125-5142.
43 Howitt, R. E. (1995). Positive mathematical programming. American journal of agricultural economics, 77(2), 329-342.
44 Huang, S.-L., Kao, W.-C., & Lee, C.-L. (2007). Energetic mechanisms and development of an urban landscape system. Ecological Modelling, 201(3-4), 495-506.
45 Hurlimann, A., Moosavi, S., & Browne, G. R. (2021). Urban planning policy must do more to integrate climate change adaptation and mitigation actions. Land Use Policy, 101, 105188.
46 IPCC. (2023). Climate Change 2023 Synthesis Report.
47 Itami, R. M. (1994). Simulating spatial dynamics: cellular automata theory. Landscape and Urban Planning, 30(1-2), 27-47.
48 Jana, M., & Sar, N. (2016). Modeling of hotspot detection using cluster outlier analysis and Getis-Ord Gi* statistic of educational development in upper-primary level, India. Modeling Earth Systems and Environment, 2(2), 60.
49 Kappes, B. B., Kuplich, T. M., da Silva, T. S., & Weber, E. J. (2024). Using multilayer perceptron and similarity-weighted machine learning algorithms to reconstruct the past: A case study of the agricultural expansion on grasslands in the Uruguayan savannas [Article]. Integrated Environmental Assessment and Management, 20(4), 1140-1155. https://doi.org/10.1002/ieam.4852
50 Karimi, H., Jafarnezhad, J., Khaledi, J., & Ahmadi, P. (2018). Monitoring and prediction of land use/land cover changes using CA-Markov model: a case study of Ravansar County in Iran. Arabian Journal of Geosciences, 11, 1-9.
51 Khoshnood Motlagh, S., Sadoddin, A., Haghnegahdar, A., Razavi, S., Salmanmahiny, A., & Ghorbani, K. (2021). Analysis and prediction of land cover changes using the land change modeler (LCM) in a semiarid river basin, Iran. Land Degradation & Development, 32(10), 3092-3105.
52 Kim, Y., Newman, G., & Güneralp, B. (2020). A Review of Driving Factors, Scenarios, and Topics in Urban Land Change Models [Review]. Land, 9(8), 22, Article 246. https://doi.org/10.3390/land9080246
53 Kiziridis, D. A., Mastrogianni, A., Pleniou, M., Tsiftsis, S., Xystrakis, F., & Tsiripidis, I. (2023). Improving the predictive performance of CLUE-S by extending demand to land transitions: The trans-CLUE-S model. Ecological Modelling, 478, 110307.
54 Kura, A. L., & Beyene, D. L. (2020). Cellular automata Markov chain model based deforestation modelling in the pastoral and agro-pastoral areas of southern Ethiopia. Remote Sensing Applications: Society and Environment, 18, 100321.
55 Lai, L., Huang, X., Yang, H., Chuai, X., Zhang, M., Zhong, T., Chen, Z., Chen, Y., Wang, X., & Thompson, J. R. (2016). Carbon emissions from land-use change and management in China between 1990 and 2010. Science Advances, 2(11), e1601063.
56 Lambin, E. F., & Geist, H. J. (2008). Land-use and land-cover change: local processes and global impacts. Springer Science & Business Media.
57 Landis, J. D. (1994). The California urban futures model: a new generation of metropolitan simulation models. Environment and Planning B: Planning and Design, 21(4), 399-420.
58 Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. biometrics, 159-174.
59 Leta, M. K., Demissie, T. A., & Tränckner, J. (2021). Modeling and prediction of land use land cover change dynamics based on land change modeler (Lcm) in nashe watershed, upper blue nile basin, Ethiopia. Sustainability, 13(7), 3740.
60 Li, L., Lei, Y., Wu, S., He, C., Chen, J., & Yan, D. (2018). Impacts of city size change and industrial structure change on CO2 emissions in Chinese cities. Journal of Cleaner Production, 195, 831-838.
61 Li, R., Li, L., & Wang, Q. (2022). The impact of energy efficiency on carbon emissions: evidence from the transportation sector in Chinese 30 provinces. Sustainable Cities and Society, 82, 103880.
62 Li, R., Wang, Q., & Guo, J. (2024). Revisiting the environmental Kuznets curve (EKC) hypothesis of carbon emissions: exploring the impact of geopolitical risks, natural resource rents, corrupt governance, and energy intensity. Journal of Environmental Management, 351, 119663.
63 Li, X., Liu, Z., Li, S., Li, Y., & Wang, W. (2023). Urban Land Carbon Emission and Carbon Emission Intensity Prediction Based on Patch-Generating Land Use Simulation Model and Grid with Multiple Scenarios in Tianjin. Land, 12(12), 2160.
64 Li, X., Wang, Y., Li, J., & Lei, B. (2016). Physical and socioeconomic driving forces of land‐use and land‐cover changes: A case study of Wuhan City, China. Discrete Dynamics in Nature and Society, 2016(1), 8061069.
65 Liu, J., Yu, Q., Chen, Y., & Liu, J. (2022). The impact of digital technology development on carbon emissions: A spatial effect analysis for China. Resources, Conservation and Recycling, 185, 106445.
66 Liu, X., Wang, M., Qiang, W., Wu, K., & Wang, X. (2020). Urban form, shrinking cities, and residential carbon emissions: Evidence from Chinese city-regions. Applied Energy, 261, 114409. https://doi.org/https://doi.org/10.1016/j.apenergy.2019.114409
67 Long, Y., Liu, X., Luo, S., Luo, T., Hu, S., Zheng, Y., Shao, J., & Liu, X. (2023). Evolution and prediction of urban fringe areas based on logistic–CA–Markov models: The case of Wuhan City. Land, 12(10), 1874.
68 Losiri, C., Nagai, M., Ninsawat, S., & Shrestha, R. P. (2016). Modeling urban expansion in Bangkok metropolitan region using demographic–economic data through cellular automata-Markov chain and multi-layer perceptron-Markov chain models. Sustainability, 8(7), 686.
69 Lucchitta, B., Palermo, V., Melica, G., Molteni, T., Burro, A., Bertoldi, P., & Croci, E. (2024). Are European cities achieving emission reduction commitments? A comparative analysis under the Covenant of Mayors initiative. Heliyon, 10(1).
70 Mas, J.-F., Kolb, M., Paegelow, M., Olmedo, M. T. C., & Houet, T. (2014). Inductive pattern-based land use/cover change models: A comparison of four software packages. Environmental Modelling & Software, 51, 94-111.
71 Mieszkowski, P., & Mills, E. S. (1993). The causes of metropolitan suburbanization. Journal of Economic perspectives, 7(3), 135-147.
72 Moghadam, H. S., & Helbich, M. (2013). Spatiotemporal urbanization processes in the megacity of Mumbai, India: A Markov chains-cellular automata urban growth model. Applied Geography, 40, 140-149.
73 Moran, P. A. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17-23.
74 Mozaffaree Pour, N., & Oja, T. (2021). Prediction power of logistic regression (LR) and Multi-Layer perceptron (MLP) models in exploring driving forces of urban expansion to be sustainable in estonia. Sustainability, 14(1), 160.
75 Mozumder, C., Tripathi, N. K., & Losiri, C. (2016). Comparing three transition potential models: A case study of built-up transitions in North-East India. Computers, Environment and Urban Systems, 59, 38-49. https://doi.org/10.1016/j.compenvurbsys.2016.04.009
76 Mueller, N., Rojas-Rueda, D., Khreis, H., Cirach, M., Andrés, D., Ballester, J., Bartoll, X., Daher, C., Deluca, A., & Echave, C. (2020). Changing the urban design of cities for health: The superblock model. Environment international, 134, 105132.
77 Mumtaz, F., Tao, Y., de Leeuw, G., Zhao, L., Fan, C., Elnashar, A., Bashir, B., Wang, G., Li, L., & Naeem, S. (2020). Modeling spatio-temporal land transformation and its associated impacts on land surface temperature (LST). Remote Sensing, 12(18), 2987.
78 Ngoy, K. I., Qi, F., & Shebitz, D. J. (2021). Analyzing and Predicting Land Use and Land Cover Changes in New Jersey Using Multi-Layer Perceptron–Markov Chain Model. Earth, 2(4), 845-870. https://www.mdpi.com/2673-4834/2/4/50
79 Parker, D. C., Manson, S. M., Janssen, M. A., Hoffmann, M., & Deadman, P. (2001). Multi-Agent systems for the simulation of land-use and land-cover change: A review. Center for the Study of Institutions, Population and Environmental Change, Indiana University, Bloomington, IN.
80 Pendall, R. (1999). Do land-use controls cause sprawl? Environment and Planning B: Planning and Design, 26(4), 555-571.
81 Peng, B., Tong, X., Cao, S., Li, W., & Xu, G. (2020). Carbon emission calculation method and low-carbon technology for use in expressway construction. Sustainability, 12(8), 3219.
82 Peng, L. L. H., Jiang, Z., Yang, X., He, Y., Xu, T., & Chen, S. S. (2020). Cooling effects of block-scale facade greening and their relationship with urban form. Building and Environment, 169, 106552. https://doi.org/https://doi.org/10.1016/j.buildenv.2019.106552
83 Pontius Jr, R. G., & Schneider, L. C. (2001). Land-cover change model validation by an ROC method for the Ipswich watershed, Massachusetts, USA. Agriculture, ecosystems & environment, 85(1-3), 239-248.
84 Pontius, R. G. (2000). Quantification error versus location error in comparison of categorical maps. Photogrammetric engineering and remote sensing, 66(8), 1011-1016.
85 Potere, D., & Schneider, A. (2007). A critical look at representations of urban areas in global maps. GeoJournal, 69(1), 55-80.
86 Rahnama, M. R. (2021). Forecasting land-use changes in Mashhad Metropolitan area using Cellular Automata and Markov chain model for 2016-2030. Sustainable Cities and Society, 64, 102548. https://doi.org/https://doi.org/10.1016/j.scs.2020.102548
87 Rogan, J., Franklin, J., Stow, D., Miller, J., Woodcock, C., & Roberts, D. (2008). Mapping land-cover modifications over large areas: A comparison of machine learning algorithms. Remote Sensing of Environment, 112(5), 2272-2283.
88 Rong, T. Q., Zhang, P. Y., Zhu, H. R., Jiang, L., Li, Y. Y., & Liu, Z. Y. (2022). Spatial correlation evolution and prediction scenario of land use carbon emissions in China [Article]. Ecological Informatics, 71, 14, Article 101802. https://doi.org/10.1016/j.ecoinf.2022.101802
89 Ruben, G. B., Zhang, K., Dong, Z., & Xia, J. (2020). Analysis and projection of land-use/land-cover dynamics through scenario-based simulations using the CA-Markov model: A case study in guanting reservoir basin, China. Sustainability, 12(9), 3747.
90 Santé, I., García, A. M., Miranda, D., & Crecente, R. (2010). Cellular automata models for the simulation of real-world urban processes: A review and analysis. Landscape and Urban Planning, 96(2), 108-122. https://doi.org/10.1016/j.landurbplan.2010.03.001
91 Shen, Y.-S., Lin, Y.-C., Cui, S., Li, Y., & Zhai, X. (2022). Crucial factors of the built environment for mitigating carbon emissions. Science of The Total Environment, 806, 150864. https://doi.org/https://doi.org/10.1016/j.scitotenv.2021.150864
92 Simkin, R. D., Seto, K. C., McDonald, R. I., & Jetz, W. (2022). Biodiversity impacts and conservation implications of urban land expansion projected to 2050. Proceedings of the National Academy of Sciences, 119(12), e2117297119.
93 Solaimani, K., & Darvishi, S. (2024). Comparative analysis of land use changes modeling based-on new hybrid models and CA-Markov in the Urmia lake basin. Advances in Space Research, 74(8), 3749-3764. https://doi.org/https://doi.org/10.1016/j.asr.2024.06.078
94 Song, W., & Deng, X. (2017). Land-use/land-cover change and ecosystem service provision in China. Science of The Total Environment, 576, 705-719.
95 Song, X.-P., Hansen, M. C., Stehman, S. V., Potapov, P. V., Tyukavina, A., Vermote, E. F., & Townshend, J. R. (2018). Global land change from 1982 to 2016. Nature, 560(7720), 639-643.
96 Sun, C., Zhang, Y., Ma, W., Wu, R., & Wang, S. (2022). The impacts of urban form on carbon emissions: A comprehensive review. Land, 11(9), 1430.
97 Sun, W., & Huang, C. (2020). How does urbanization affect carbon emission efficiency? Evidence from China. Journal of Cleaner Production, 272, 122828.
98 Surya, B., Ahmad, D. N. A., Sakti, H. H., & Sahban, H. (2020). Land use change, spatial interaction, and sustainable development in the metropolitan urban areas, South Sulawesi Province, Indonesia. Land, 9(3), 95.
99 Tang, J., & Di, L. (2019). Past and future trajectories of farmland loss due to rapid urbanization using Landsat imagery and the Markov-CA model: a case study of Delhi, India. Remote Sensing, 11(2), 180.
100 Theobald, D. M., & Gross, M. D. (1994). EML: a modeling environment for exploring landscape dynamics. Computers, Environment and Urban Systems, 18(3), 193-204.
101 Townshend, J. R., & Justice, C. O. (2002). Towards operational monitoring of terrestrial systems by moderate-resolution remote sensing. Remote Sensing of Environment, 83(1-2), 351-359.
102 Verburg, P. H., Van De Steeg, J., Veldkamp, A., & Willemen, L. (2009). From land cover change to land function dynamics: A major challenge to improve land characterization. Journal of Environmental Management, 90(3), 1327-1335.
103 Viana, C. M., Oliveira, S., Oliveira, S. C., & Rocha, J. (2019). Land use/land cover change detection and urban sprawl analysis. In Spatial modeling in GIS and R for earth and environmental sciences (pp. 621-651). Elsevier.
104 Wang, S.-H., Huang, S.-L., & Huang, P.-J. (2018). Can spatial planning really mitigate carbon dioxide emissions in urban areas? A case study in Taipei, Taiwan. Landscape and Urban Planning, 169, 22-36.
105 Wei, B., Kasimu, A., Reheman, R., Zhang, X., Zhao, Y., Aizizi, Y., & Liang, H. (2023). Spatiotemporal characteristics and prediction of carbon emissions/absorption from land use change in the urban agglomeration on the northern slope of the Tianshan Mountains. Ecological Indicators, 151, 110329.
106 White, R., & Engelen, G. (1993). Cellular automata and fractal urban form: a cellular modelling approach to the evolution of urban land-use patterns. Environment and Planning A, 25(8), 1175-1199.
107 Xia, C. Y., Dong, Z. Y. Z., Wu, P., Dong, F., Fang, K., Li, Q., Li, X. S., Shao, Z., & Yu, Z. N. (2022). How urban land-use intensity affected CO2 emissions at the county level: Influence and prediction [Article]. Ecological Indicators, 145, 10, Article 109601. https://doi.org/10.1016/j.ecolind.2022.109601
108 Xu, L., Liu, X., Tong, D., Liu, Z., Yin, L., & Zheng, W. (2022). Forecasting urban land use change based on cellular automata and the PLUS model. Land, 11(5), 652.
109 Xu, T. T., Gao, J., & Coco, G. (2019). Simulation of urban expansion via integrating artificial neural network with Markov chain - cellular automata. International Journal of Geographical Information Science, 33(10), 1960-1983. https://doi.org/10.1080/13658816.2019.1600701
110 Yang, F., He, F., Li, S., Li, M., & Wu, P. (2023). A new estimation of carbon emissions from land use and land cover change in China over the past 300 years. Science of The Total Environment, 863, 160963. https://doi.org/https://doi.org/10.1016/j.scitotenv.2022.160963
111 Yang, J., Su, J., Chen, F., Xie, P., & Ge, Q. (2016). A Local Land Use Competition Cellular Automata Model and Its Application. ISPRS International Journal of Geo-Information, 5(7), 106. https://www.mdpi.com/2220-9964/5/7/106
112 Yang, W., & Zhou, S. (2020). Using decision tree analysis to identify the determinants of residents’ CO2 emissions from different types of trips: A case study of Guangzhou, China. Journal of Cleaner Production, 277, 124071.
113 Yang, Y., & Takase, T. (2024). Spatial characteristics of carbon dioxide emission intensity of urban road traffic and driving factors: Road network and land use. Sustainable Cities and Society, 113, 105700. https://doi.org/https://doi.org/10.1016/j.scs.2024.105700
114 Yao, Y., Sun, Z. H., Li, L. L., Cheng, T., Chen, D. S., Zhou, G. X., Liu, C. X., Kou, S. H., Chen, Z. H., & Guan, Q. F. (2023). CarbonVCA: A cadastral parcel-scale carbon emission forecasting framework for peak carbon emissions [Article]. Cities, 138, 12, Article 104354. https://doi.org/10.1016/j.cities.2023.104354
115 Ye, C., & Ming, T. (2023). Land use carbon emissions estimation and carbon emissions control strategy effect scenario simulation in Zhejiang province. Heliyon, 9(11).
116 Ye, H., Ren, Q., Hu, X., Lin, T., Shi, L., Zhang, G., & Li, X. (2018). Modeling energy-related CO2 emissions from office buildings using general regression neural network. Resources, Conservation and Recycling, 129, 168-174. https://doi.org/https://doi.org/10.1016/j.resconrec.2017.10.020
117 Yeh, A. G.-O., & Li, X. (2003). Simulation of development alternatives using neural networks, cellular automata, and GIS for urban planning. Photogrammetric Engineering & Remote Sensing, 69(9), 1043-1052.
118 Yeh, A. G., Li, X., & Xia, C. (2021). Cellular automata modeling for urban and regional planning. Urban informatics, 865-883.
119 Zhang, H., Peng, J., Wang, R., Zhang, J., & Yu, D. (2021). Spatial planning factors that influence CO2 emissions: A systematic literature review. Urban Climate, 36, 100809. https://doi.org/https://doi.org/10.1016/j.uclim.2021.100809
120 Zhang, X., Zhou, J., & Song, W. (2020). Simulating urban sprawl in china based on the artificial neural network-cellular automata-Markov model. Sustainability, 12(11), 4341.
121 Zhang, X. P., Liao, Q. H., Zhao, H., & Li, P. (2022). Vector maps and spatial autocorrelation of carbon emissions at land patch level based on multi-source data. Frontiers in Public Health, 10, Article 1006337. https://doi.org/10.3389/fpubh.2022.1006337
122 Zhang, Y., Long, H., Tu, S., Ge, D., Ma, L., & Wang, L. (2019). Spatial identification of land use functions and their tradeoffs/synergies in China: Implications for sustainable land management. Ecological Indicators, 107, 105550.
123 Zhang, Y., Teoh, B. K., Wu, M., Chen, J., & Zhang, L. (2023). Data-driven estimation of building energy consumption and GHG emissions using explainable artificial intelligence. Energy, 262, 125468. https://doi.org/https://doi.org/10.1016/j.energy.2022.125468
124 Zhang, Y., Wang, P. C., Wang, T. W., Cai, C. F., Li, Z. X., & Teng, M. J. (2018). Scenarios Simulation of Spatio-Temporal Land Use Changes for Exploring Sustainable Management Strategies [Article]. Sustainability, 10(4), 17, Article 1013. https://doi.org/10.3390/su10041013
125 Zhou, C., Wang, S., & Wang, J. (2019). Examining the influences of urbanization on carbon dioxide emissions in the Yangtze River Delta, China: Kuznets curve relationship. Science of The Total Environment, 675, 472-482. https://doi.org/https://doi.org/10.1016/j.scitotenv.2019.04.269
126 Zhou, L., Dang, X., Sun, Q., & Wang, S. (2020). Multi-scenario simulation of urban land change in Shanghai by random forest and CA-Markov model. Sustainable Cities and Society, 55, 102045. https://doi.org/https://doi.org/10.1016/j.scs.2020.102045
127 Zhou, R., Zhang, H., Ye, X.-Y., Wang, X.-J., & Su, H.-L. (2016). The delimitation of urban growth boundaries using the CLUE-S land-use change model: Study on Xinzhuang Town, Changshu City, China. Sustainability, 8(11), 1182.
128 Zhu, Y., & Hu, Y. (2023). The Correlation between Urban Form and Carbon Emissions: A Bibliometric and Literature Review. Sustainability, 15(18), 13439.
中文文獻
129 內政部(2023)。土地利用領域氣候變遷調適行動方案(112-115)。內政部。
130 台灣電力公司。各縣市售電資訊. https://service.taipower.com.tw/country-power-sales/
131 李俊霖(2008)。社經代謝作用與土地利用變遷之整合與空間動態(未發表的博士論文)。國立臺北大學都市計劃學系博士班。
132 周天穎、簡甫任、雷祖強(2003)。都市地區土地利用變遷量化分析之研究。臺灣土地研究,6(1),105-130。
133 林書任(2011)。大眾運輸導向發展策略對城市區域土地使用形態之影響─以新台南市為例(未發表的碩士論文)。國立成功大學都市計劃學系碩士班。
134 邱薏潔(2024)。氣候變遷與都市發展對洪災危害度之空間區位影響探討―以臺北市為例(未發表的碩士論文)。國立成功大學都市計劃學系碩士班。
135 秦寂梅(2022)。沿海高風險區後撤性調適策略對洪災風險變遷之影響—以嘉義縣東石鄉與布袋鎮為例(未發表的碩士論文)。國立成功大學都市計劃學系碩士班。
136 國家發展委員會(2022)。臺灣2050淨零排放路徑及策略總說明。國家發展委員會。
137 張政亮(2006)。馬可夫鏈模型(Markvo Chain Model)在地理學研究之運用。國教新知,53(1),72-86。
138 張洲滄、吳佩儒、&林子平(2021)。臺中市及臺南市都會區碳排放量與土地發展相關指標關係之研究。建築學報,(117),97-116。
139 張學聖、劉佩佳(2015)。考量空間關聯之地區洪災脆弱性研究以雲林縣易淹水地區為例.地理學報,(79),1-29。
140 張學聖、魏良諭(2020)。國土空間規劃回應氣候變遷之比較分析-以台南市為例.規劃學報,38(2),1-28。
141 連美綺、吳治達、莊永忠、廖學誠(2011)。應用馬可夫模式分析桃園海岸地區土地利用變遷之研究。工程環境會刊,(26),71-85。
142 陳柏宏(2017).以防災為導向之都市聰明萎縮管理-以嘉義地區土地使用調適為例(未發表的碩士論文)。國立成功大學都市計劃學系碩士班。
143 黃子芩(2021)。探討萎縮都市下的土地使用變遷模式(未發表的碩士論文)。國立臺北大學都市計劃學系碩士班。
144 黃書禮、蔡靜如(2000)。台北盆地土地利用變遷趨勢之研究。都市與計劃,27(1),1-23。
145 黃國慶、士樑(2009)。台北都會區土地使用/覆蓋變遷驅動力之空間近鄰效果探討。都市與計劃,36(4),415-443。
146 鄒克萬、顧嘉安、幸福(2014)。以馬可夫鍊細胞自動機模型模擬極端洪水對都市土地利用型態之影響:以台北市為例.都市與計劃,41(1),43-66。
147 趙益群、李欣輯、蕭逸華、永明(2023)。土地利用變遷工具於未來都市淹水衝擊之應用研究.中國土木水利工程學刊,35(7),695-704。
148 劉小蘭、沈育生、蔡杰廷(2016)。都會區綠地變遷趨勢及其驅動因素之探討-以臺北都會區為例。都市與計劃,43(2),189-227。
149 環境部氣候變遷署(2024)。2024 年中華民國國家溫室氣體排放清冊報告。環境部氣候變遷署。
150 顧嘉安(2010)。以馬可夫鍊細胞自動機模型模擬極端洪水對都市土地利用型態之影響─以台北市為例(未發表的碩士論文)。國立成功大學都市計劃學系碩士班。
151 顧嘉安(2020)。以agent-based model模擬都市發展複雜時空動態之初探.都市與計劃,47(2),149-172。
152 顧翰琳、邱英浩、林淑雯、劉育芸(2025)。雙北土地使用型態與建築用電碳排放空間關聯之研究。都市與計劃,52(1),77-101。
校內:2027-07-31公開