Abstract
Land-use change is a crucial driver for achieving a sustainable future. However, the uncertainties of socioeconomic development could lead to different changes in the future land-use patterns. Using a spatial downscaling framework, this study aims to explore possible land-use patterns that can help achieve sustainable development in the Guangdong–Hong Kong–Macao Greater Bay Area, China (the Greater Bay Area). The framework combines the global Shared Socioeconomic Pathways (SSPs) scenarios with local land planning policies to model land-use changes. First, the Land Change Modeler was used to analyze the land-use changes from 2000 to 2010 and build transition potential submodels each of which demonstrates transition potential of different land-use classes. Second, future projections were made for the “business-as-usual” scenario and five localized SSP scenarios that were downscaled from global scenarios and modified based on the local land planning policy. Hong Kong was considered a typical case in the Greater Bay Area that could be used to demonstrate the application of the projected land-use maps by comparing the biocapacity and ecological footprint and estimating the carbon emissions associated with land use. The results of the future projections of land use made under six future scenarios indicated that there is a significant expansion in the urban area under all the scenarios, with varying degrees of decrease in cropland and forest among the different scenarios. Moreover, a land-use change also led to the change in local biocapacity and carbon emissions. Our analysis indicated that in achieving sustainable development not only urban area and cropland should be involved for consideration but should also cover the balance between all land-use classes, and three policy implications were proposed based on our findings.
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Acheampong M, Yu Q, Enomah LD, Anchang J, Eduful M (2018) Land use/cover change in Ghana’s oil city: assessing the impact of neoliberal economic policies and implications for sustainable development goal number one—a remote sensing and GIS approach. Land Use Policy 73:373–384
Allen C, Metternicht G, Wiedmann T (2017) An iterative framework for national scenario modelling for the Sustainable Development Goals (SDGs). Sustain Dev 25(5):372–385
Asia N (2020) Hong Kong wants a massive new island, but where will it get the sand? https://asia.nikkei.com/Opinion/Hong-Kong-wants-a-massive-new-island-but-where-will-it-get-the-sand. Accessed 9 December 2020
Aung TS, Fischer TB, Buchanan J (2020) Land use and land cover changes along the China-Myanmar Oil and Gas pipelines–Monitoring infrastructure development in remote conflict-prone regions. PLoS ONE 15(8):e027806
Borucke M, Moore D, Cranston G, Gracey K, Iha K, Larson J, Lazarus E, Morales JC, Wackernagel M, Galli A (2013) Accounting for demand and supply of the biosphere’s regenerative capacity: The National Footprint Accounts’ underlying methodology and framework. Ecol Ind 24:518–533
Brownlee J (2020) How to calculate precision, recall, and F-measure for imbalanced classification. https://machinelearningmastery.com/precision-recall-and-f-measure-for-imbalanced-classification/. Accessed 28 December 2020
Cao W, Yuan X (2019) Region-county characteristic of spatial-temporal evolution and influencing factor on land use-related CO2 emissions in Chongqing of China, 1997–2015. J Clean Prod 231:619–632
Caspersen JP, Pacala SW, Jenkins JC, Hurtt GC, Moorcroft PR, Birdsey RA (2000) Contributions of land-use history to carbon accumulation in US forests. Science 290(5494):1148–1151
Central Committee of the Communist Party of China (2019) Outline Development Plan for the Guangdong-Hong Kong-Macao Greater Bay Area. https://www.bayarea.gov.hk/filemanager/en/share/pdf/Outline_Development_Plan.pdf. Accessed 4 August 2020
Chapa F, Hariharan S, Hack J (2019) A new approach to high-resolution urban land use classification using open access software and true color satellite images. Sustainability 11(19):5266
Chen W, Ye X, Li J, Fan X, Liu Q, Dong W (2019a) Analyzing requisition–Compensation balance of farmland policy in China through telecoupling: a case study in the middle reaches of Yangtze River Urban Agglomerations. Land Use Policy 83:134–146
Chen Y, Li X, Liu X, Zhang Y, Huang M (2019b) Tele-connecting China’s future urban growth to impacts on ecosystem services under the shared socioeconomic pathways. Sci Total Environ 652:765–779
Chen M, Vernon CR, Graham NT, Hejazi M, Huang M, Cheng Y, Calvin K (2020a) Global land use for 2015–2100 at 0.05° resolution under diverse socioeconomic and climate scenarios. Sci Data 7(1):1–11
Chen Y, Li X, Huang K, Luo M, Gao M (2020b) High-resolution gridded population projections for China under the shared socioeconomic pathways. Earth’s Future 8(6):e2020EF001491
Chu L, Sun T, Wang T, Li Z, Cai C (2018) Evolution and prediction of landscape pattern and habitat quality based on CA-Markov and InVEST model in hubei section of three gorges reservoir area (TGRA). Sustainability 10(11):3854
Clark labs (2020) Land change modeler in Terrset. https://clarklabs.org/terrset/land-change-modeler/. Accessed 30 December 2020
DasGupta R, Hashimoto S, Okuro T, Basu M (2019) Scenario-based land change modelling in the Indian Sundarban delta: an exploratory analysis of plausible alternative regional futures. Sustain Sci 14(1):221–240
Department of Natural Resources of Guangdong Province (2017) Land planning of Guangdong Province (2016–2030). http://nr.gd.gov.cn/attachment/0/188/188208/506474.pdf. Accessed 23 November 2020
Department of Natural Resources of Guangdong Province (2018) Land Planning of Guangdong Province (2016–2035). http://nr.gd.gov.cn/attachment/0/187/187183/586580.pdf. Accessed 23 November 2020
Díaz S, Settele J, Brondízio E, Ngo H, Guèze M, Agard J, Arneth A, Balvanera P, Brauman K, Butchart S, Chan K, Garibaldi L, Ichii K, Liu J, Subramanian S, Midgley G, Miloslavich P, Molnár Z, Obura D, Pfaff A, Polasky S, Purvis A, Razzaque J, Reyers B, Chowdhury R, Shin Y, Visseren-Hamakers I, Willis K, and Zayas C (eds) (2020) Summary for policymakers of the global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. IPBES secretariat, Bonn, Germany. https://doi.org/10.5281/zenodo.3553579
Dong N, You L, Cai W, Li G, Lin H (2018) Land use projections in China under global socioeconomic and emission scenarios: Utilizing a scenario-based land-use change assessment framework. Glob Environ Chang 50:164–177
DSEC (2020) Statistical website of the Guangdong-Hong Kong-Macao Greater Bay Area. https://www.dsec.gov.mo/BayArea/en-US/#home. Accessed 8 December 2020
Eastman JR (2015) TerrSet manual. Clark University, Worcester
Eastman J, Toledano J (2018) A short presentation of the Land Change Modeler (LCM). In: Olmedo MTC, Paegelow M, Mas J-F, Escobar F (eds) Geomatic approaches for modeling land change scenarios. Springer, Cham, pp 499–505
Estoque RC, Ooba M, Avitabile V, Hijioka Y, DasGupta R, Togawa T, Murayama Y (2019) The future of Southeast Asia’s forests. Nat Commun 10(1):1–12
Global footprint network (2019) Working guidebook to the national footprint and biocapacity accounts. https://www.footprintnetwork.org/content/uploads/2019/05/National_Footprint_Accounts_Guidebook_2019.pdf. Accessed 28 August 2020
Global footprint network (2020a) Glossary. https://www.footprintnetwork.org/resources/glossary/. Accessed 1 January 2021
Global footprint network (2020b) Open data platform. https://data.footprintnetwork.org/?_ga=2.62753900.1324893541.1611108161-1081994963.1587799037#/. Accessed 20 January 2021
Gomes L, Bianchi F, Cardoso I, Schulte R, Arts B, Fernandes Filho E (2020) Land use and land cover scenarios: an interdisciplinary approach integrating local conditions and the global shared socioeconomic pathways. Land Use Policy 97:104723
Greater Bay Area (2018) Overview. https://www.bayarea.gov.hk/en/about/overview.html. Accessed 3 August 2020
Gu X (2019) Agricultural cooperation and development in Guangdong-Hong Kong-Macao Great Bay Area: status quo, problems and countermeasures. J Political Sci Law 4:5–7
Gu B, Zhang X, Bai X, Fu B, Chen D (2019) Four steps to food security for swelling cities. Nature Publishing Group, Berlin
Han L, Meng P, Jiang R, Xu B, Zhang B, Chen M (2018) Logical root, pattern exploration and management innovation of balancing cultivated land occupation and reclamation in the new era. China Land Sci 32(6):90–96
Hasan S, Shi W, Zhu X, Abbas S, Khan HUA (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
Hashimoto S, DasGupta R, Kabaya K, Matsui T, Haga C, Saito O, Takeuchi K (2019) Scenario analysis of land-use and ecosystem services of social-ecological landscapes: implications of alternative development pathways under declining population in the Noto Peninsula. Jpn Sustain Sci 14(1):53–75
Hausfather Z (2018) Explainer: How ‘Shared Socioeconomic Pathways’ explore future climate change. https://www.carbonbrief.org/explainer-how-shared-socioeconomic-pathways-explore-future-climate-change. Accessed 19 August 2020
He C, Li J, Zhang X, Liu Z, Zhang D (2017) Will rapid urban expansion in the drylands of northern China continue: a scenario analysis based on the land use scenario dynamics-urban model and the shared socioeconomic pathways. J Clean Prod 165:57–69
Hewitt RJ, Compagnucci AB, Castellazzi M, Dunford RW, Harrison PA, Pedde S, Gimona A (2020) Impacts and trade-offs of future land use and land cover change in Scotland: spatial simulation modelling of shared socioeconomic pathways at regional scales. https://doi.org/10.31235/osf.io/fc6he
Houghton RA, House J, Pongratz J, Van Der Werf G, DeFries R, Hansen M, Le Quéré C, Ramankutty N (2012) Carbon emissions from land use and land-cover change. Biogeosciences 12:5125–5142
IIASA (2018) SSP database version 2.0. https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=welcome. Accessed 3 September 2020
IPBES (2016a) Methodological assessment report on scenarios and models of biodiversity and ecosystem services—summary for policymakers. https://ipbes.net/sites/default/files/downloads/pdf/spm_deliverable_3c_scenarios_20161124.pdf. Accessed 30 August 2020
IPBES (2016b) Scenarios and models: new scenarios and supporting assessments. https://ipbes.net/scenarios-models. Accessed 30 August 2020
IPBES (2020) IPBES-IPCC co-sponsored workshop: spotlighting the interactions of the science of biodiversity and climate change. https://ipbes.net/ipbes-ipcc-cosponsored-workshop-media-release. Accessed 4 January 2021
Jiao M, Hu M, Xia B (2019) Spatiotemporal dynamic simulation of land-use and landscape-pattern in the Pearl River Delta, China. Sustain Cities Soc 49:101581
Jones B, O’Neill BC (2016) Spatially explicit global population scenarios consistent with the Shared Socioeconomic Pathways. Environ Res Lett 11(8):084003
Kindu M, Schneider T, Döllerer M, Teketay D, Knoke T (2018) Scenario modelling of land use/land cover changes in Munessa-Shashemene landscape of the Ethiopian highlands. Sci Total Environ 622:534–546
Kok K, Pedde S, Gramberger M, Harrison PA, Holman IP (2019) New European socio-economic scenarios for climate change research: operationalising concepts to extend the shared socio-economic pathways. Reg Environ Change 19(3):643–654
Kumar KS, Bhaskar PU, Padmakumari K (2015) Application of land change modeler for prediction of future land use land cover: a case study of Vijayawada City. Int J Adv Technol Eng Sci 3(1):773–783
Leung D, Newsam S (2012) Exploring geotagged images for land-use classification. In: Proceedings of the ACM multimedia 2012 workshop on Geotagging and its applications in multimedia pp 3–8
Li X, Chen Y (2020) Projecting the future impacts of China’s cropland balance policy on ecosystem services under the shared socioeconomic pathways. J Clean Prod 250:119489
Li J, Wang J (2019) Identification, classification, and mapping of coastal ecosystem services of the Guangdong, Hong Kong, and Macao Great Bay Area. Acta Ecol Sin 9(17):6393–6403. https://doi.org/10.5846/stxb201805231130
Li X, Yu L, Sohl T, Clinton N, Li W, Zhu Z, Liu X, Gong P (2016) A cellular automata downscaling based 1 km global land use datasets (2010–2100). Sci Bull 61(21):1651–1661
Li X, Zhou Y, Eom J, Yu S, Asrar GR (2019) Projecting global urban area growth through 2100 based on historical time series data and future Shared Socioeconomic Pathways. Earth’s Future 7(4):351–362
Liao W, Liu X, Xu X, Chen G, Liang X, Zhang H, Li X (2020) Projections of land use changes under the plant functional type classification in different SSP-RCP scenarios in China. Sci Bull 65:1935
Lin W, Sun Y, Nijhuis S, Wang Z (2020) Scenario-based flood risk assessment for urbanizing deltas using future land-use simulation (FLUS): Guangzhou Metropolitan Area as a case study. Sci Total Environ 739:139899
Liu M, Li W, Xie G (2010) Estimation of China ecological footprint production coefficient based on net primary productivity. Chin J Ecol 29(3):592–597
Liu X, Liang X, Li X, Xu X, Ou J, Chen Y, Li S, Wang S, Pei F (2017) A future land use simulation model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects. Landsc Urban Plan 168:94–116
Liu X, Huang Y, Xu X, Li X, Li X, Ciais P, Lin P, Gong K, Ziegler AD, Chen A (2020) High-spatiotemporal-resolution mapping of global urban change from 1985 to 2015. Nat Sustain 3:1–7
Lu Y, Wu P, Ma X, Li X (2019) Detection and prediction of land use/land cover change using spatiotemporal data fusion and the Cellular Automata–Markov model. Environ Monit Assess 191(2):68
Lundquist CJ, Pereira HM, Porto CADV, Peterson GD, Karlsson-Vinkhuyzen S, Pereira L, Acosta LA, Akcakaya HR, Davies KK, Den Belder E (2020) A pluralistic Nature Futures Framework. https://edisciplinas.usp.br/pluginfile.php/4884426/mod_resource/content/1/Relat%C3%B3rio%20IPBES.pdf. Accessed 24 July 2021
Mallapaty S (2020) How China could be carbon neutral by mid-century. Nature 586(7830):482–483
Mas J, Paegelow M, Olmedo MC (2018) LUCC modeling approaches to calibration. Geomatic approaches for modeling land change scenarios. Springer, Cham, pp 11–25
Mohammady M, Moradi H, Zeinivand H, Temme A (2015) A comparison of supervised, unsupervised and synthetic land use classification methods in the north of Iran. Int J Environ Sci Technol 12(5):1515–1526
Moran DD, Wackernagel M, Kitzes JA, Goldfinger SH, Boutaud A (2008) Measuring sustainable development—Nation by nation. Ecol Econ 64(3):470–474
Nakicenovic N, Lempert RJ, Janetos AC (2014) A framework for the development of new socio-economic scenarios for climate change research: introductory essay. Clim Change 122(3):351–361
Nilsson AE, Bay-Larsen I, Carlsen H, van Oort B, Bjørkan M, Jylhä K, Klyuchnikova E, Masloboev V, van der Watt L-M (2017) Towards extended shared socioeconomic pathways: a combined participatory bottom-up and top-down methodology with results from the Barents region. Glob Environ Chang 45:124–132
O’Neill BC, Kriegler E, Ebi KL, Kemp-Benedict E, Riahi K, Rothman DS, van Ruijven BJ, van Vuuren DP, Birkmann J, Kok K (2017) The roads ahead: narratives for shared socioeconomic pathways describing world futures in the 21st century. Glob Environ Chang 42:169–180
O’Neill BC, Carter TR, Ebi K, Harrison PA, Kemp-Benedict E, Kok K, Kriegler E, Preston BL, Riahi K, Sillmann J (2020) Achievements and needs for the climate change scenario framework. Nat Clim Change 10:1–11
Olmedo MC, Mas J (2018) Markov CHAIN. Geomatic approaches for modeling land change scenarios. Springer, Cham, pp 441–445
Palazzo A, Vervoort JM, Mason-D’Croz D, Rutting L, Havlík P, Islam S, Bayala J, Valin H, Kadi HAK, Thornton P (2017) Linking regional stakeholder scenarios and shared socioeconomic pathways: quantified West African food and climate futures in a global context. Glob Environ Chang 45:227–242
People's government of Guangdong province (2019) Issuance of the work plan for the compilation of land planning of Guangdong Province (2020–2035). http://www.gd.gov.cn/zwgk/zcjd/snzcsd/content/post_2530532.html. Accessed 30 December 2020 (in Chinese)
People's government of Guangdong province (2020) The Ministry of Natural Resources issued nine guidelines to support the Greater Bay Area and Shenzhen in exploring the reform of natural resources, and Guangdong province in exploring the built-up land trading mechanism on the provincial scale. http://www.gd.gov.cn/gdywdt/gdyw/content/post_3058085.html. Accessed 13 October 2020 (in Chinese)
Pereira LM, Davies KK, den Belder E, Ferrier S, Karlsson-Vinkhuyzen S, Kim H, Kuiper JJ, Okayasu S, Palomo MG, Pereira HM (2020) Developing multiscale and integrative nature–people scenarios using the Nature Futures Framework. People Nat 2:1172
Popp A, Calvin K, Fujimori S, Havlik P, Humpenöder F, Stehfest E, Bodirsky BL, Dietrich JP, Doelmann JC, Gusti M (2017) Land-use futures in the shared socio-economic pathways. Glob Environ Chang 42:331–345
Rawat J, Kumar M (2015) Monitoring land use/cover change using remote sensing and GIS techniques: a case study of Hawalbagh block, district Almora, Uttarakhand, India. Egypt J Remote Sens Space Sci 18(1):77–84
Riahi K, Van Vuuren DP, Kriegler E, Edmonds J, Oneill BC, Fujimori S, Bauer N, Calvin K, Dellink R, Fricko O (2017) The shared socioeconomic pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Glob Environ Change 42:153–168
Rimal B, Zhang L, Keshtkar H, Haack BN, Rijal S, Zhang P (2018) Land use/land cover dynamics and modeling of urban land expansion by the integration of cellular automata and markov chain. ISPRS Int J Geo Inf 7(4):154
Ritchie H, Roser M (2017) CO2 and greenhouse gas emissions. https://ourworldindata.org/greenhouse-gas-emissions Accessed 19 January 2021
Rosa IM, Pereira HM, Ferrier S, Alkemade R, Acosta LA, Akcakaya HR, Den Belder E, Fazel AM, Fujimori S, Harfoot M (2017) Multiscale scenarios for nature futures. Nat Ecol Evol 1(10):1416–1419
Rosa I, Lundquist CJ, Ferrier S, Alkemade R, Castro PF, Joly CA (2020) Increasing capacity to produce scenarios and models for biodiversity and ecosystem services. Biota Neotropica 20 (suppl 1). https://doi.org/10.1590/1676-0611-BN-2020-1101
Ruben GB, 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
Saito O, Kamiyama C, Hashimoto S, Matsui T, Shoyama K, Kabaya K, Uetake T, Taki H, Ishikawa Y, Matsushita K (2019) Co-design of national-scale future scenarios in Japan to predict and assess natural capital and ecosystem services. Sustain Sci 14(1):5–21
Saxena S (2018) Precision vs recall. https://medium.com/@shrutisaxena0617/precision-vs-recall-386cf9f89488. Accessed 28 December 2020
ScienceNet.cn (2019) How to develop the eco-agricultural pathway in the Greater Bay Area. http://news.sciencenet.cn/sbhtmlnews/2019/7/348220.shtm?id=348220. Accessed 13 October 2020 (in Chinese)
Shi H, Mu X, Zhang Y, Lv M (2012) Effects of different land use patterns on carbon emission in Guangyuan city of Sichuan province. Bull Soil Water Conserv 32(3):101–106 ((in Chinese))
Shi X, Matsui T, Machimura T, Gan X, Hu A (2020) Toward sustainable development: decoupling the high ecological footprint from human society development: a case study of Hong Kong. Sustainability 12(10):4177
Shoyama K, Matsui T, Hashimoto S, Kabaya K, Oono A, Saito O (2019) Development of land-use scenarios using vegetation inventories in Japan. Sustain Sci 14(1):39–52
Solecki WD, Oliveri C (2004) Downscaling climate change scenarios in an urban land use change model. J Environ Manage 72(1–2):105–115
Song S, Liu Z, He C, Lu W (2020) Evaluating the effects of urban expansion on natural habitat quality by coupling localized shared socioeconomic pathways and the land use scenario dynamics-urban model. Ecol Indic 112:106071
Tang H, Sang L, Yun W (2020) China’s cultivated land balance policy implementation dilemma and direction of scientific and technological innovation. Bull Chin Acad Sci 35(5):637–644. https://doi.org/10.16418/j.issn.1000-3045.20200313002
The Environmental Protection Department of Hong Kong (2017) Hong Kong greenhouse gas inventory for 2015 released. https://www.info.gov.hk/gia/general/201707/10/P2017071000628.htm?fontSize=1. Accessed 1 January 2021
The National People's Congress of the PRC (2019) Land Administration Law of the PRC. http://www.npc.gov.cn/npc/c30834/201909/d1e6c1a1eec345eba23796c6e8473347.shtml. Accessed 13 October 2020 (in Chinese)
United States Environmental Protection Agency (2018) Overview of greenhouse gases. https://www.epa.gov/ghgemissions/overview-greenhouse-gases. Accessed 19 January 2021
Valdivia RO, Antle JM, Rosenzweig C, Ruane AC, Vervoort J, Ashfaq M, Hathie I, Tui SH-K, Mulwa R, Nhemachena C (2015) Representative agricultural pathways and scenarios for regional integrated assessment of climate change impacts, vulnerability, and adaptation. Handb Clim Change Agroecosyst 3:101–156
Van Ruijven BJ, Levy MA, Agrawal A, Biermann F, Birkmann J, Carter TR, Ebi KL, Garschagen M, Jones B, Jones R (2014) Enhancing the relevance of shared socioeconomic pathways for climate change impacts, adaptation and vulnerability research. Clim Change 122(3):481–494
van Vliet J (2019) Direct and indirect loss of natural area from urban expansion. Nat Sustain 2(8):755–763
Vázquez-Quintero G, Solís-Moreno R, Pompa-García M, Villarreal-Guerrero F, Pinedo-Alvarez C, Pinedo-Alvarez A (2016) Detection and projection of forest changes by using the Markov Chain Model and cellular automata. Sustainability 8(3):236
Wackernagel M, Rees WE (1996) Our ecological footprint: reducing human impact on the Earth. New Society Publishers, Gabriola Island
Wan J, Han Y (2019) Inter-regional comparison of edible agricultural product supply capability and agricultural industrial chain function in the Greater Bay Area. J South China Univ Technol (soc Sci Ed) 21(6):9–20. https://doi.org/10.19366/j.cnki.1009-055X.2019.06.002
Wang L, Yang Y, Feng Z, You Z (2014) Prediction of China’s population in 2020 and 2030 on county scale. Geogr Res 33(2):310
Wang R, Derdouri A, Murayama Y (2018) Spatiotemporal simulation of future land use/cover change scenarios in the Tokyo metropolitan area. Sustainability 10(6):2056
Wang C, Wang Y, Wang R, Zheng P (2018) Modeling and evaluating land-use/land-cover change for urban planning and sustainability: a case study of Dongying city, China. J Clean Prod 172:1529–1534
Worldpop (2020) Population counts-individual countries 2000–2020 UN adjusted (100m resoultion). https://www.worldpop.org/geodata/listing?id=69. Accessed 28 August 2020
WWF (2013) Hong Kong ecological footprint report 2013. https://www.footprintnetwork.org/content/images/article_uploads/hong_kong_ecological_footprint_report_2013.pdf. Accessed 9 December 2020
WWF (2019) Guangdong-Hong Kong-Macao Greater Bay Area Ecological Footprint Report. http://www.wwf-opf.org.cn/upload/news/609b9697e4abe961e224e401852e41f2.pdf. Accessed 13 October 2020 (in Chinese)
Yang G, Zhu W, Wen Y, Lin Y (2019) Spatial differentiation in the intensify and efficiency of carbon emission from land use in Guangdong province in past two decades. Ecol Environ Sci 28(2):332–340 ((in Chinese))
Yang B, Chen X, Wang Z, Li W, Zhang C, Yao X (2020) Analyzing land use structure efficiency with carbon emissions: a case study in the Middle Reaches of the Yangtze River, China. J Clean Prod 274:123076
Yirsaw E, Wu W, Shi X, Temesgen H, Bekele B (2017) Land use/land cover change modeling and the prediction of subsequent changes in ecosystem service values in a coastal area of China, the Su-Xi-Chang Region. Sustainability 9(7):1204
Zhang R, Matsushima K, Kobayashi K (2018) Can land use planning help mitigate transport-related carbon emissions? A case of Changzhou. Land Use Policy 74:32–40
Zhu Y, Deng X, Newsam S (2019) Fine-grained land use classification at the city scale using ground-level images. IEEE Trans Multimedia 21(7):1825–1838
Acknowledgements
We specially appreciate Prof. Jun Chen (scientist of National Geomatics Center of China and member of Chinese Academy of Engineering), Dr. Yimin Chen (Sun Yat-sen University), and Dr. Fanglei Zhong (Lanzhou University) for their help in data sharing; we thank Dr. Xun Liang (China University of Geosciences) for his advice on the conversion cost matrix. And we are also grateful to Dr. Wanhui Huang (Research Institute for Humanity and Nature, Japan) for sharing her expertise on land-use simulation. This work was supported by the Environment Research and Technology Development Fund (S-15, JPMEERF16S11500).
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Shi, X., Matsui, T., Haga, C. et al. A scenario- and spatial-downscaling-based land-use modeling framework to improve the projections of plausible futures: a case study of the Guangdong–Hong Kong–Macao Greater Bay Area, China . Sustain Sci 16, 1977–1998 (2021). https://doi.org/10.1007/s11625-021-01011-z
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DOI: https://doi.org/10.1007/s11625-021-01011-z