{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:27:23Z","timestamp":1760239643842,"version":"build-2065373602"},"reference-count":37,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2020,12,13]],"date-time":"2020-12-13T00:00:00Z","timestamp":1607817600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012456","name":"Chinese National Funding of Social Sciences","doi-asserted-by":"publisher","award":["15AGL002"],"award-info":[{"award-number":["15AGL002"]}],"id":[{"id":"10.13039\/501100012456","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p> Evaluation of agricultural investment climate has essential reference value for site selection, operation and risk management of agricultural outward foreign direct investment projects. This study builds a back propagation neural network-based agricultural investment climate evaluation model, which has 22 indicators of four subsystems that take political climate, economic climate, social climate, and technological climate as the input vector, and agricultural investment climate rating as the output vector, to evaluate the agricultural investment climate in 16 Central and Eastern European (CEE) countries. The overall spatial distribution characteristics demonstrate that the best agricultural investment climate is in the three Baltic countries, followed by the Visegrad Group and Slovenia sector, and then the Balkan littoral countries. The findings may provide insights for entrepreneurs who aim to invest in agriculture abroad and contribute to the improvement of these countries\u2019 investment climate.<\/jats:p>","DOI":"10.3390\/a13120336","type":"journal-article","created":{"date-parts":[[2020,12,13]],"date-time":"2020-12-13T20:56:57Z","timestamp":1607893017000},"page":"336","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Evaluation of Agricultural Investment Climate in CEE Countries: The Application of Back Propagation Neural Network"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7083-2687","authenticated-orcid":false,"given":"Ru","family":"Guo","sequence":"first","affiliation":[{"name":"School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaodong","family":"Qiu","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiyi","family":"He","sequence":"additional","affiliation":[{"name":"School of Science, Royal Melbourne Institute of Technology, Melbourne 3000, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,13]]},"reference":[{"key":"ref_1","unstructured":"Cotula, L. 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