Climate-smart agriculture (CSA) technology holds tremendous potential in minimizing climate risks, carbon sequestration, ensuring food security, and achieving sustainable intensification goals. Adopting CSA technology has become essential for realizing sustainable agricultural systems. This study employs the Heckman two-stage model to analyze how livelihood capital influences the sustained adoption of CSA technology, utilizing micro-level survey data from 221 new agricultural business entities in Wuhu City, Anhui Province. The results demonstrate significant positive correlations between human capital, physical capital, social capital, financial capital, natural capital, and digital capital among these entities and their willingness to adopt CSA technologies. Therefore, it is crucial to enhance public education, technical training, and incentive policies, leverage digital technologies to empower agricultural development, and stimulate the vitality of new agricultural business entities. These measures will facilitate the continuous diffusion of CSA technology, driving China's green agricultural transformation and high-quality development.

Yunni Wang, Ting Wang. The Influence of Livelihood Capital on the Continuous Adoption Willingness of Climate Smart Agricultural Technology by New Agricultural Operators —— Taking Traditional Agricultural Areas in Southern China as an Example (2025), Vol. 4, Issue 1: 11-19. https://doi.org/10.38007/RE.2025.040102.

[1] Zhang, P., J. Zhang and M. Chen, Economic impacts of climate change on agriculture: The importance of additional climatic variables other than temperature and precipitation. Journal of Environmental Economics and Management, 2017. 83: p. 8-31.

[2] Karimi, V., E. Karami and M. Keshavarz, Climate change and agriculture: Impacts and adaptive responses in Iran. Journal of Integrative Agriculture, 2018. 17(1): p. 1-15.

[3] Chandra, A., K.E. McNamara and P. Dargusch, Climate-smart agriculture: perspectives and framings. Climate Policy, 2018. 18(4): p. 526-541.

[4] Challinor, A.J., L.N. Arenas-Calles and S. Whitfield, Measuring the Effectiveness of Climate-Smart Practices in the Context of Food Systems: Progress and Challenges. Frontiers in Sustainable Food Systems, 2022. 6.

[5] Khoza, S., et al., A gender-differentiated analysis of climate-smart agriculture adoption by smallholder farmers: application of the extended technology acceptance model. Gender Technology & Development, 2021. 25(1): p. 1-21.

[6] Zakaria, A., et al., Adoption of climate-smart agricultural practices among farm households in Ghana: The role of farmer participation in training programmes. Technology in Society, 2020. 63.

[7] Mujeyi, A., M. Mudhara and M.J. Mutenje, Adoption determinants of multiple climate smart agricultural technologies in Zimbabwe: Considerations for scaling-up and out. African Journal of Science Technology Innovation & Development, 2020. 12(6): p. 735-746.

[8] Makate, C., et al., Increasing resilience of smallholder farmers to climate change through multiple adoptions of proven climate-smart agriculture innovations. Lessons from Southern Africa. Journal of Environmental Management, 2019. 231: p. 858-868.

[9] Khatri-Chhetri, A., et al., Stakeholders prioritization of climate-smart agriculture interventions: Evaluation of a framework. Agricultural Systems, 2019. 174: p. 23-31.

[10] Tankha, S., D. Fernandes and N.C. Narayanan, Overcoming barriers to climate smart agriculture in India. International Journal of Climate Change Strategies and Management, 2020. 12(1): p. 108-127.

[11] Jellason, N.P., J.S. Conway and R.N. Baines, Understanding impacts and barriers to adoption of climate-smart agriculture (CSA) practices in North-Western Nigerian drylands. Journal of Agricultural Education & Extension, 2021. 27(1): p. 55-72.

[12] Long, T.B., V. Blok and I. Coninx, Barriers to the adoption and diffusion of technological innovations for climate-smart agriculture in Europe: evidence from the Netherlands, France, Switzerland and Italy. Journal of Cleaner Production, 2016. 112: p. 9-21.

[13] Guo Lipeng, He Meiying and Chen Shiyu, the Impact of Household Livelihood Capital on Different Technology Adoption Behaviors with Different Preferences ——Analysis Based on the Sustainable Livelihood Framework. China Agricultural Resources and Regional Planning, 2022.43(02): pp. 197-205. (in Chinese)

[14] Tang Qing, Research status and future key trends of sustainable livelihood. Earth Science Advances, 2015.30(07):823-833. (in Chinese)

[15] Shi Hengtong et al., the Impact of livelihood capital on proximity environment behavior of indigenous residents in the qinling national park creation area —— an analysis based on the perspective of equity perception. Journal of Natural Resources, 2024.39(10): pp. 2335-2349. (in Chinese)

[16] Lax, J. and M. Köthke, livelihood strategies and forest product utilisation of rural households in Nepal. Small-Scale Forestry, 2017. 16(4): p. 505-520.

[17] Tan Linyi et al., The impact of livelihood capital on farmers' willingness to exit homesteads ——. a dual perspective of level and structure. China Land Science, 2024.38(03): pp. 26-37. (in Chinese)

[18] Ren Li, Zhang Miao and Chen Yinrong, Livelihood Capital, perception of multiple values of cultivated land and farmers' land transfer willingness. Observation on typical areas during the transition period from poverty alleviation to rural revitalization. China Land Science, 2022, 36(06): pp. 56-65. (in Chinese)

[19] Yu Lifu, Cao Dayu and Liao Bing. The influence of livelihood capital and ecological cognition on farmers' willingness to adopt green production technologies. China Agricultural Resources and Regional Planning: pp. 1-15. (in Chinese)

[20] Gong Xinshu and Zhao Liyu, Digital economy, human capital and high-quality integration of urban-rural areas —— hypothesis-based hypotheses and tests. Rural Economy, 2024(07): pp. 58-66. (in Chinese)

[21] Jiang Xiaohui and Niu Jiawei, Digital economy, rural human capital and urban-rural income gap. China Human Resources Development, 2024.41(09): pp. 80-92. (in Chinese)

[22] Bhattacherjee, A., Understanding information systems continuance: An expectation-confirmation model. Mis Quarterly, 2001. 25(3): p. 351-370.