Can digital technology promote sustainable agriculture? Empirical evidence from urban China

References

• Ali, B., & Dahlhaus, P. (2022). The role of FAIR data towards sustainable agricultural performance: A systematic Literature Review. Agriculture, 12(2), 309. https://doi.org/10.3390/agriculture12020309
   Google Scholar
• Balmford, B., Green, R. E., Onial, M., Phalan, B., & Balmford, A. (2018). How imperfect can land sparing be before land sharing is more favourable for wild species? The Journal of Applied Ecology, 56(1), 73–26. https://doi.org/10.1111/1365-2664.13282
   Web of Science ®Google Scholar
• Balmford, A., Green, R. E., & Scharlemann, J. P. (2005). Sparing land for nature: Exploring the potential impact of changes in agricultural yield on the area needed for crop production. Global Change Biology, 11(10), 1594–1605. https://doi.org/10.1111/j.1365-2486.2005.001035.x
   Web of Science ®Google Scholar
• Belkhir, L., & Elmeligi, A. (2018). Assessing ICT global emissions footprint: Trends to 2040 & recommendations. Journal of Cleaner Production, 177, 448–463. https://doi.org/10.1016/j.jclepro.2017.12.239
   Web of Science ®Google Scholar
• Cai, J., Ma, E., Lin, J., Liao, L., & Han, Y. (2020). Exploring global food security pattern from the perspective of spatio-temporal evolution. Journal of Geographical Sciences, 30(2), 179–196. https://doi.org/10.1007/s11442-020-1722-y
   Web of Science ®Google Scholar
• Caputo, P., Zagarella, F., Cusenza, M. A., Mistretta, M., & Cellura, M. (2020). Energy-environmental assessment of the UIA-OpenAgri case study as urban regeneration project through agriculture. The Science of the Total Environment, 729, 138819. https://doi.org/10.1016/j.scitotenv.2020.138819
   PubMed Web of Science ®Google Scholar
• Chandio, A. A., Akram, W., Sargani, G. R., Twumasi, M. A., & Ahmad, F. (2022). Assessing the impacts of meteorological factors on soybean production in China: What role can agricultural subsidy play? Ecological Informatics, 71, 101778. https://doi.org/10.1016/j.ecoinf.2022.101778
   Web of Science ®Google Scholar
• Chandio, A. A., Bashir, U., Akram, W., Usman, M., Ahmad, M., & Jiang, Y. (2023). What role do international remittance inflows play in boosting agricultural productivity? Empirical analysis of emerging Asian economies. International Journal of Emerging Markets. https://doi.org/10.1108/IJOEM-06-2022-1019
   Web of Science ®Google Scholar
• Chandio, A. A., Gokmenoglu, K. K., Khan, I., Ahmad, F., & Jiang, Y. (2023). Does internet technology usage improve food production? Recent evidence from major rice-producing provinces of China. Computers and Electronics in Agriculture, 211, 108053. https://doi.org/10.1016/j.compag.2023.108053
   Web of Science ®Google Scholar
• Chandio, A. A., Gokmenoglu, K. K., Sethi, N., Ozdemir, D., & Jiang, Y. (2023). Examining the impacts of technological advancement on cereal production in ASEAN countries: Does information and communication technology matter? The European Journal of Agronomy, 144, 126747. https://doi.org/10.1016/j.eja.2023.126747
   Web of Science ®Google Scholar
• Chandio, A. A., Sethi, N., Dash, D. P., & Usman, M. (2022). Towards sustainable food production: What role ICT and technological development can play for cereal production in Asian–7 countries? Computers and Electronics in Agriculture, 202, 107368. https://doi.org/10.1016/j.compag.2022.107368
   Web of Science ®Google Scholar
• Chantarat, S., & Barrett, C. B. (2012). Social network capital, economic mobility and poverty traps. Journal of Economic Inequality, 10(3), 299–342. https://doi.org/10.1007/s10888-011-9164-5
   Web of Science ®Google Scholar
• Chantarat, P., & Skuterud, M. (2004). Internet job search and unemployment durations. The American Economic Review, 94(1), 218–232. https://doi.org/10.1257/000282804322970779
   Web of Science ®Google Scholar
• Chen, Y., Li, S., & Cheng, L. (2020). Evaluation of cultivated land use efficiency with environmental constraints in the Dongting Lake Eco-economic Zone of Hunan province, China. Land, 9(11), 440. https://doi.org/10.3390/land9110440
   Web of Science ®Google Scholar
• Chen, Z., Sarkar, A., Rahman, A., Li, X., & Xia, X. (2022). Exploring the drivers of green agricultural development (GAD) in China: A spatial association network structure approaches. Land Use Policy, 112, 105827. https://doi.org/10.1016/j.landusepol.2021.105827
   Web of Science ®Google Scholar
• Driouchi, A., Azelmad, E. M., & Anders, G. C. (2006). An econometric analysis of the role of knowledge in economic performance. The Journal of Technology Transfer, 31(2), 241–255. https://doi.org/10.1007/s10961-005-6109-9
   Google Scholar
• Dunlap, R. E., Beus, C. E., Howell, R. E., & Waud, J. (1993). What is sustainable agriculture? An empirical examination of faculty and farmer definitions. Journal of Sustainable Agriculture, 3(1), 5–41. https://doi.org/10.1300/J064v03n01_03
   Web of Science ®Google Scholar
• Elhorst, J. P. (2014). Matlab software for spatial panels. International Regional Science Review, 68(2), 401–420.
   Google Scholar
• FAO. (2009). FAO's Director-General on How to feed the World 2050. Population and Development Review, 35(4), 837–839. https://doi.org/10.1111/j.17284457.2009.00312.x
   Web of Science ®Google Scholar
• Forman, C., Goldfarb, A., & Greenstein, S. (2012). The internet and local wages: A puzzle. American Economic Review, 102(1), 556–575. https://doi.org/10.1257/aer.102.1.556
   Web of Science ®Google Scholar
• García, V. R., Gaspart, F., Kastner, T., & Meyfroidt, P. (2020). Agricultural intensification and land use change: Assessing country-level induced intensification, land sparing and rebound effect. Environmental Research Letters, 15(8), 085007. https://doi.org/10.1088/1748-9326/ab8b14
   Web of Science ®Google Scholar
• Gómez-Limón, J. A., & Sanchez-Fernandez, G. (2010). Empirical evaluation of agricultural sustainability using composite indicators. Ecological Economics: The Journal of the International Society for Ecological Economics, 69(5), 1062–1075. https://doi.org/10.1016/j.ecolecon.2009.11.027
   Web of Science ®Google Scholar
• Guo, H., Gu, F., Peng, Y., Deng, X., & Guo, L. (2022). Does digital inclusive finance effectively promote agricultural green development?—A case study of China. International Journal of Environmental Research and Public Health, 19(12), 6982. https://doi.org/10.3390/ijerph19126982
   PubMed Web of Science ®Google Scholar
• Hall, J., & Vredenburg, H. (2003). The challenges of innovating for sustainable development. MIT Sloan Management Review, 45(1), 61–68.
   Web of Science ®Google Scholar
• Hampton, S. E., Strasser, C. A., Tewksbury, J. J., Gram, W. K., Budden, A. E., Batcheller, A. L., Duke, C. S., & Porter, J. H. (2013). Big data and the future of ecology. Frontiers in Ecology and the Environment, 11(3), 156–162. https://doi.org/10.1890/120103
   Web of Science ®Google Scholar
• Hansen, B. E. (1999). Threshold effects in non-dynamic panels: Estimation, testing, and inference. Journal of Econometrics, 93(2), 345–368. https://doi.org/10.1016/S0304-4076(99)00025-1
   Web of Science ®Google Scholar
• Hickey, L. T., Hafeez, A. N., Robinson, H., Jackson, S. A., Leal-Bertioli, S. C. M., Tester, M., Gao, C., Godwin, I. D., Hayes, B. J., & Wulff, B. B. H. (2019). Breeding crops to feed 10 billion. Nature Biotechnology, 37(7), 744–754. https://doi.org/10.1038/s41587-019-0152-9
   PubMed Web of Science ®Google Scholar
• Hidalgo, A., Gabaly, S., Morales-Alonso, G., & Urueña, A. (2020). The digital divide in light of sustainable development: An approach through advanced machine learning techniques. Technological Forecasting and Social Change, 150, 119754. https://doi.org/10.1016/j.techfore.2019.119754
   Web of Science ®Google Scholar
• Hong, Y., Heerink, N., Zhao, M., & van der Werf, W. (2019). Intercropping contributes to a higher technical efficiency in smallholder farming: Evidence from a case study in Gaotai County, China. Agricultural Systems, 173, 317–324. https://doi.org/10.1016/j.agsy.2019.03.007
   Web of Science ®Google Scholar
• Hrustek, L. (2020). Sustainability driven by agriculture through digital transformation. Sustainability, 12(20), 8596. https://doi.org/10.3390/su12208596
   Web of Science ®Google Scholar
• Hu, X., Wang, Z., & Liu, J. (2022). The impact of digital finance on household insurance purchases: Evidence from micro data in China. The Geneva Papers on Risk and Insurance - Issues and Practice, 47(3), 538–568. https://doi.org/10.1057/s41288-022-00267-5
   Web of Science ®Google Scholar
• Jiang, Q., Li, J., Si, H., & Su, Y. (2022). The impact of the digital economy on agricultural green development: Evidence from China. Agriculture, 12(8), 1107. https://doi.org/10.3390/agriculture12081107
   Google Scholar
• Kamble, S. S., Gunasekaran, A., & Gawankar, S. A. (2020). Achieving sustainable performance in a data-driven agriculture supply chain: A review for research and applications. International Journal of Production Economics, 219, 179–194. https://doi.org/10.1016/j.ijpe.2019.05.022
   Web of Science ®Google Scholar
• Keller, W. (2002). Trade and the transmission of technology. Journal of Economic Growth, 7(1), 5–24. https://doi.org/10.1023/A:1013461025733
   Web of Science ®Google Scholar
• Khattak, S. I., Ahmad, M., Haq, Z., Shaofu, G., & Hang, J. (2022). On the goals of sustainable production and the conditions of environmental sustainability: Does cyclical innovation in green and sustainable technologies determine carbon dioxide emissions in G-7 economies. Sustainable Production and Consumption, 29, 406–420. https://doi.org/10.1016/j.spc.2021.10.022
   Web of Science ®Google Scholar
• Klerkx, L., Jakku, E., & Labarthe, P. (2019). A review of social science on digital agriculture, smart farming and agriculture 4.0: New contributions and a future research agenda. NJAS: Wageningen Journal of Life Sciences, 90–91(1), 1–16. https://doi.org/10.1016/j.njas.2019.100315
   Google Scholar
• Klerkx, L., & Rose, D. (2020). Dealing with the game-changing technologies of agriculture 4.0: How do we manage diversity and responsibility in food system transition pathways? Global Food Security, 24, 100347. https://doi.org/10.1016/j.gfs.2019.100347
   Google Scholar
• Knickel, K., Ashkenazy, A., Chebach, T. C., & Parrot, N. (2017). Agricultural modernization and sustainable agriculture: Contradictions and complementarities. International Journal of Agricultural Sustainability, 15(5), 575–592. https://doi.org/10.1080/14735903.2017.1373464
   Web of Science ®Google Scholar
• Lal, R. (2010). Managing soils and ecosystems for mitigating anthropogenic carbon emissions and advancing global food security. BioScience, 60(9), 708–721. https://doi.org/10.1525/bio.2010.60.9.8
   Web of Science ®Google Scholar
• Lange, S., Pohl, J., & Santarius, T. (2020). Digitalization and energy consumption. Does ICT reduce energy demand? Ecological Economics: The Journal of the International Society for Ecological Economics, 176, 106760. https://doi.org/10.1016/j.ecolecon.2020.106760
   Web of Science ®Google Scholar
• Lawin, K. G., & Tamini, L. D. (2019). Tenure security and farm efficiency analysis correcting for biases from observed and unobserved variables: Evidence from benin. Journal of Agricultural Economics, 70(1), 116–134. https://doi.org/10.1111/1477-9552.12275
   Web of Science ®Google Scholar
• Leng, C., Ma, W., Tang, J., & Zhu, Z. (2020). ICT adoption and income diversification among rural households in China. Applied Economics, 52(33), 3614–3628. https://doi.org/10.1080/00036846.2020.1715338
   Web of Science ®Google Scholar
• Lin, Q., Dai, X., Cheng, Q., & Lin, W. (2022). Can digital inclusive finance promote food security? Evidence from China. Sustainability, 14(20), 13160. https://doi.org/10.3390/su142013160
   Web of Science ®Google Scholar
• Liu, Y., & Zhou, M. (2023). Can rural e-commerce narrow the urban–rural income gap? Evidence from coverage of Taobao villages in China. China Agricultural Economic Review, 15(3), 580–603. https://doi.org/10.1108/CAER-09-2022-0221
   Google Scholar
• Li, F., Zang, D., Chandio, A. A., Yang, D., & Jiang, Y. (2023). Farmers’ adoption of digital technology and agricultural entrepreneurial willingness: Evidence from China. Technology in Society, 73, 102253. https://doi.org/10.1016/j.techsoc.2023.102253
   Web of Science ®Google Scholar
• Li, Y., Zhang, W., Ma, L., Wu, L., Shen, J., Davies, W. J., Oenema, O., Zhang, F., & Dou, Z. (2014). An analysis of China’s grain production: Looking back and looking forward. Food and Energy Security, 3(1), 19–32. https://doi.org/10.1002/fes3.41
   Web of Science ®Google Scholar
• Luan, B., Zou, H., & Huang, J. (2023). Digital divide and household energy poverty in China. Energy Economics, 119, 106543. https://doi.org/10.1016/j.eneco.2023.106543
   Web of Science ®Google Scholar
• Malmodin, J., & Lundén, D. (2018). The energy and carbon footprint of the global ICT and E&M sectors 2010–2015. Sustainability, 10(9), 3027. https://doi.org/10.3390/su10093027
   Web of Science ®Google Scholar
• Ma, W., & Wang, X. (2020). Internet use, sustainable agricultural practices and rural incomes: Evidence from China. The Australian Journal of Agricultural and Resource Economics, 64(4), 1087–1112. https://doi.org/10.1111/1467-8489.12390
   Web of Science ®Google Scholar
• Meena, R. S., Kumar, S., Datta, R., Lal, R., Vijayakumar, V., Brtnicky, M., Sharma, M. P., Yadav, G. S., Jhariya, M. K., Jangir, C. K., Pathan, S., Dokulilova, T., Pecina, V., & Marfo, T. (2020). Impact of agrochemicals on soil microbiota and Management: A Review. Land, 9(2), 34. https://doi.org/10.3390/land9020034
   Web of Science ®Google Scholar
• Mili, S., & Martínez-Vega, J. (2019). Accounting for regional heterogeneity of agricultural sustainability in Spain. Sustainability, 11(2), 299. https://doi.org/10.3390/su11020299
   Web of Science ®Google Scholar
• Millan, J. M., Lyalkov, S., Burke, A., Millan, A., & van Stel, A. (2021). Digital divide’among European entrepreneurs: Which types benefit most from ICT implementation? Journal of Business Research, 125, 533–547. https://doi.org/10.1016/j.jbusres.2019.10.034
   Web of Science ®Google Scholar
• Munyegera, G. K., & Matsumoto, T. (2018). ICT for financial access: Mobile money and the financial behavior of rural households in Uganda. Review of Development Economics, 22(1), 45–66. https://doi.org/10.1111/rode.12327
   Web of Science ®Google Scholar
• Mushi, G. E., DiMarzo Serugendo, G., & Burgi, P. Y. (2022). Digital technology and services for sustainable agriculture in Tanzania: A Literature Review. Sustainability, 14(4), 2415. https://doi.org/10.3390/su14042415
   Web of Science ®Google Scholar
• NFU. (2019). The future of food 2040. Retrieved May 7, 2023, from https://www.nfuonline.com/nfu-online/news/the-future-of-food-2040/.
   Google Scholar
• Nie, P., Ma, W., & Sousa-Poza, A. (2021). The relationship between smartphone use and subjective well-being in rural China. Electronic Commerce Research, 21(4), 983–1009. https://doi.org/10.1007/s10660-020-09397-1
   Web of Science ®Google Scholar
• Nunn, N., & Qian, N. (2014). US food aid and civil conflict. The American Economic Review, 104(6), 1630–1666. https://doi.org/10.1257/aer.104.6.1630
   Web of Science ®Google Scholar
• OECD. (2019). Agri-environmental indicators. http://www.oecd.org/greengrowth/sustainable-agriculture/agri-environmentalindicators.Html
   Google Scholar
• Park, C. Y., & Mercado, R. V. (2016). Does financial inclusion reduce poverty and income inequality in developing. Palgrave Macmillan UK. Mercado.
   Google Scholar
• Phalan, B., Onial, M., Balmford, A., & Green, R. E. (2011). Reconciling food production and biodiversity conservation: Land sharing and land sparing compared. Science, 333(6047), 1289–1291. https://doi.org/10.1126/science.1208742
   PubMed Web of Science ®Google Scholar
• Qin, T., Wang, L., Zhou, Y., Guo, L., Jiang, G., & Zhang, L. (2022). Digital technology-and-services-driven sustainable transformation of agriculture: Cases of China and the EU. Agriculture, 12(2), 297. https://doi.org/10.3390/agriculture12020297
   Google Scholar
• Robinson, S. V., Nguyen, L. H., & Galpern, P. (2022). Livin’on the edge: Precision yield data shows evidence of ecosystem services from field boundaries. Agriculture, Ecosystems & Environment, 333, 107956. https://doi.org/10.1016/j.agee.2022.107956
   Web of Science ®Google Scholar
• Roussaki, I., Doolin, K., Skarmeta, A., Routis, G., Lopez-Morales, J. A., Claffey, E., Mora, M., & Martinez, J. A. (2022). Building an interoperable space for smart agriculture. Digital Communications and Networks, 9(1), 183–193. https://doi.org/10.1016/j.dcan.2022.02.004
   Web of Science ®Google Scholar
• Sanders, R. (2006). A market road to sustainable agriculture? Ecological agriculture, green food and organic agriculture in China. Development & Change, 37(1), 201–226. https://doi.org/10.1111/j.0012-155X.2006.00475.x
   Web of Science ®Google Scholar
• Shen, J., Zhu, Q., Jiao, X., Ying, H., Wang, H., Wen, X., Xu, W., Li, T., Cong, W., & Liu, X. (2020). Agriculture green development: A model for China and the world. Frontiers of Agricultural Science and Engineering, 7(1), 5–13. https://doi.org/10.15302/J-FASE-2019300
   Web of Science ®Google Scholar
• Shin, D. H., & Choi, M. J. (2015). Ecological views of big data: Perspectives and issues. Telematics and Informatics, 32(2), 311–320. https://doi.org/10.1016/j.tele.2014.09.006
   Web of Science ®Google Scholar
• Simone, C., & Christopher, S. (2003). Can information and communications technology applications contribute to poverty reduction? Lessons from rural India. Information Technology for Development, 10(2), 73–84. https://doi.org/10.1002/itdj.1590100203
   Google Scholar
• Smil, V. (1999). China’s agricultural land. China Quarterly, 158, 414–429. https://doi.org/10.1017/S030574100000583X
   Google Scholar
• Stevenson, M., & Lee, H. (2001). Indicators of sustainability as a tool in agricultural development: Partitioning scientific and participatory processes. International Journal of Sustainable Development & World Ecology, 8(1), 57–65. https://doi.org/10.1080/13504500109470063
   Web of Science ®Google Scholar
• Struik, P. C., & Kuyper, T. W. (2017). Sustainable intensification in agriculture: The richer shade of green. A review. Agronomy for Sustainable Development, 37(5), 39. https://doi.org/10.1007/s13593-017-0445-7
   Web of Science ®Google Scholar
• Trivelli, L., Apicella, A., Chiarello, F., Rana, R., Fantoni, G., & Tarabella, A. (2019). From precision agriculture to industry 4.0.: Unveiling technological connections in the agrifood sector. British Food Journal, 121(8), 1730–1743. https://doi.org/10.1108/BFJ-11-2018-0747
   Web of Science ®Google Scholar
• Tsolakis, N., Aivazidou, E., & Srai, J. S. (2019). Sensor applications in agrifood systems: Current trends and opportunities for water stewardship. Climate, 7(3), 44. https://doi.org/10.3390/cli7030044
   Web of Science ®Google Scholar
• Überbacher, R., Ceipek, R., Hautz, J., Matzler, K., & De Massis, A. (2019). Digital IoT technology, firm performance and family involvement: A resource utilization perspective. Academy of Management Proceedings, 2019(1), 14697. https://doi.org/10.5465/AMBPP.2019.14697abstract
   Google Scholar
• Van Cauwenbergh, N., Biala, K., Bielders, C., Brouckaert, V., Franchois, L., Cidad, V. G., Hermy, M., Mathijs, E., Muys, B., Reijnders, J., Sauvenier, X., Valckx, J., Vanclooster, M., Van der Veken, B., Wauters, E., & Peeters, A. (2007). SAFE—A hierarchical framework for assessing the sustainability of agricultural systems. Agriculture, Ecosystems & Environment, 120(2–4), 229–242. https://doi.org/10.1016/j.agee.2006.09.006
   Web of Science ®Google Scholar
• Wang, M., Bai, L., Gong, S., & Huang, L. (2020). Determinants of consumer food safety self-protection behavior—an analysis using grounded theory. Food Control, 113, 107198. https://doi.org/10.1016/j.foodcont.2020.107198
   Web of Science ®Google Scholar
• Wang, J., Bi, S., Li, Y., & Lyu, K. (2018). The effect of formal credit constraint on grain production. Journal of Agrotechnical Economics, 5, 28–39. https://doi.org/10.13246/j.cnki.jae.2018.05.003
   Google Scholar
• Wang, H., Grant, W. E., & Teague, R. (2020). Modeling rangelands as spatially-explicit complex adaptive systems. Journal of Environmental Management, 269, 110762. https://doi.org/10.1016/j.jenvman.2020.110762
   PubMed Web of Science ®Google Scholar
• Wang, Y., Huo, Z., Li, D., & Zhang, M. (2022). Evaluation of common prosperity level and regional difference analysis along the Yangtze river economic Belt. International Journal of Environmental Research and Public Health, 19(19), 11851. https://doi.org/10.3390/ijerph191911851
   PubMed Web of Science ®Google Scholar
• Wu, L., Zhong, Y., Shan, L., & Qin, W. (2013). Public risk perception of food additives and food scares. The case in Suzhou, China. Appetite, 70, 90–98. https://doi.org/10.1016/j.appet.2013.06.091
   PubMed Web of Science ®Google Scholar
• Xu, C. (2023). Towards balanced low-carbon development: Driver and complex network of urban-rural energy-carbon performance gap in China. Applied Energy, 333, 120663. https://doi.org/10.1016/j.apenergy.2023.120663
   Web of Science ®Google Scholar
• Xu, G., Sarkar, A., & Qian, L. (2020). Does organizational participation affect farmers’ behavior in adopting the joint mechanism of pest and disease control? A study of Meixian County, Shaanxi province. Pest Management, 77(3), 1428–1443. https://doi.org/10.1002/ps.6161
   Google Scholar
• Yilmaz, S., Haynes, K. E., & Dinc, M. (2002). Geographic and network neighbors: Spillover effects of telecommunications infrastructure. Journal of Regional Science, 42(2), 339–360. https://doi.org/10.1111/1467-9787.00262
   Web of Science ®Google Scholar
• Zhang, A., Chandio, A. A., Yang, T., Ding, Z., & Liu, Y. (2023). Examining how internet use and non-farm employment affect rural households’ income gap? Evidence from China. Frontiers in Sustainable Food Systems, 7, 1173158. https://doi.org/10.3389/fsufs.2023.1173158
   Google Scholar
• Zhang, C., & Liu, C. (2015). The impact of ICT industry on CO2 emissions: A regional analysis in China. Renew. Renewable and Sustainable Energy Reviews, 44, 12–19. https://doi.org/10.1016/j.rser.2014.12.011
   Web of Science ®Google Scholar
• Zhang, X. Z., & Xu, Z. W. (2023). Facebook and Tencent data fit a cube law better than Metcalfe’s law. Journal of Computational Science & Technology, 38(2), 219–227. https://doi.org/10.1007/s11390-022-2845-7
   Web of Science ®Google Scholar
• Zhang, Q., Zhang, F. F., Wu, G. Y., & Mai, Q. (2021). Spatial spillover effects of grain production efficiency in China: Measurement and scope. Journal of Cleaner Production, 278(1), 121062. https://doi.org/10.1016/j.jclepro.2020.121062
   Google Scholar
• Zhong, R., He, Q., & Qi, Y. (2022). Digital Economy, Agricultural Technological Progress, and Agricultural Carbon Intensity: Evidence from China. International Journal of Environmental Research and Public Health, 19(11), 6488. https://doi.org/10.3390/ijerph19116488
   PubMed Web of Science ®Google Scholar
• Zhou, Z., Liu, W., Wang, H., & Yang, J. (2022). The impact of environmental regulation on agricultural productivity: From the perspective of digital transformation. International Journal of Environmental Research and Public Health, 19(17), 10794. https://doi.org/10.3390/ijerph191710794
   PubMed Web of Science ®Google Scholar
• Zhou, C., Zhang, R., Ning, X., & Zheng, Z. (2020). Spatial-temporal characteristics in grain production and its influencing factors in the Huang-Huai-Hai plain from 1995 to 2018. International Journal of Environmental Research and Public Health, 17(24), 1–19. https://doi.org/10.3390/ijerph17249193
   Web of Science ®Google Scholar
• Zhou, L., Zhen, F., Wang, Y., & Xiong, L. (2019). Modeling the spatial formation mechanism of poverty-stricken counties in China by using geographical detector. Sustainability, 11(17), 4752. https://doi.org/10.3390/su11174752
   Web of Science ®Google Scholar