In the last decade, despite considerable research developed for the forestland leasing market, little has been published in terms of econometric results on determinants of intentions and behaviors of Chinese farmers. With respect to leasing forestland, this study uses a Bayesian logit model to examine the factors that influence farmers’ intentions, using household data collected in one county in 2017. The results show that farmers’ past experience of leasing forestlands have significant impacts on their leasing intentions. Once farmers participated in leasing in or leasing out forestland in the last five years, it was shown that they will have stronger intentions of doing so in the future. Farmers will neither lease in or out forestland if the leasing profits are less than the profits originated from forestland management. As such, household head age, household population, proportion of income from nonfarm sources to total income, and security of rights to forestland use are significant factors in influencing farmers’ decisions on leasing in forestland. On the other hand, household head age and educational level, proportion of income from nonfarm sources to total income, and importance of forestland in terms of inheritance are significant factors in influencing farmers’ decisions on leasing it out. Results imply that institutional and market factors, which have impacts on transaction costs, are important for farmers in making decisions on forestland leases. Policy implications to reduce institutional intervention are discussed.

The rapid growth of computing demand poses a material challenge to climate policy, making the productivity effects of green digital infrastructure an urgent empirical question. This study estimates whether China’s National Green Data Center Pilot Program, a policy bundling energy efficiency mandates with renewable energy procurement at the infrastructure layer, generates measurable firm-level effects on digital transformation and total factor productivity (TFP). This study treats the staggered rollout of the program across cities as a quasi-natural experiment and estimates its effect on listed firms using a two-way fixed effects difference-in-differences design over 2016 to 2023, complemented by event study analysis, placebo tests, instrumental variable diagnostics, heterogeneity robust estimators for staggered adoption, and mechanism and moderation analyses. The central finding is that pilot designation significantly accelerates firm-level digital transformation, measured by textual indicators of data value mining capability ($p<0.01$), which remains significant across specifications. Complementary evidence points to a modest positive effect on TFP, with an estimated increase of approximately 2.0 percent under the preferred city-level clustered specification, interpreted as suggestive evidence rather than the sole basis for inference. Exploratory mediation decomposition under the strong assumption of sequential ignorability indicates that digital transformation accounts for approximately 23.5 percent of the total effect, though this estimate should be treated as indicative rather than definitive. Mechanism-consistent analysis finds a marginally significant positive association with corporate ESG performance ($p<0.10$), while the association with green technology innovation is directionally positive but not statistically distinguishable from zero. Moderating effect analysis reveals that financing constraints and leverage significantly attenuate the productivity dividend, while public environmental concern provides a marginally significant amplifying effect ($p<0.10$). These results position green data center policy primarily as an upstream enabler of digital transformation that can generate productivity benefits when firms possess sufficient complementary investment capacity.