New-type urbanization is a dynamic process composed of numerous elements and multiple subsystems. Coordination and coherence of various subsystems are critical for promoting urban-rural integration and regional synergistic development and will have a complicated impact on sustainable development in the trade-off between socioeconomic growth and environmental conservation. This paper theorized the interconnected and mutually influential coupling coordination relationship between population-land-economic urbanization (PLEU) and the mechanism by which this relationship affects urban carbon emissions. The Wavelet Neural Network (WNN), Coupling Coordination Degree model, and econometric model were used to reveal this relationship and mechanism. A total of 17 cities in Shandong Province, China, were used as study subjects for empirical examination. The results show that the level of PLEU in Shandong was significantly improved from 2007 to 2016, but the gap between cities tended to further expand. The 17 cities gradually got rid of the antagonistic disorder issue of the coupling coordination relationship of PLEU from 2007 to 2016, spatially showing the agglomeration characteristics with Qingdao and Jinan as the core, respectively. The influence of the coupling coordination degree of PLEU on urban carbon emissions presents an inverted, U-shaped characteristic, that is, the total urban carbon emissions will first increase and then decrease as the improvement of the coupling coordination relationship of PLEU. China’s new-type urbanization has provided an efficient solution to sustainable development, namely planning and designing at the highest strategic level to establish scientific goals. This new-type urbanization strategy could provide an effective alternative for other emerging countries worldwide, for addressing the challenges to sustainable development.

Land costs in agricultural production are rapidly becoming apparent and increasing in light of the market’s rapid development in rural China. This study examines how agricultural operators’ farming decisions are affected by explicit and implicit costs associated with land transfers. This study first categorizes explicit and implicit land costs theoretically and demonstrates that both types of land costs affect farmers’ crop-planting selections. The study then uses the most recent household survey data from the Chinese Family Database (CFD) and the China Household Finance Survey (CHFS), released in 2017, to empirically test the theoretical analysis and examine the mechanisms underlying the impact of land costs on crop-planting selections. This study makes the case that higher land costs, both explicit and implicit, cause agricultural operators to grow more crops for cash and less for food. The impact of land costs on planting selection can vary depending on the heterogeneity of production areas and geography. In terms of production area heterogeneity, the explicit cost of land has a greater influence on planting decisions in non-major food-producing areas, whereas the implicit cost of land has a greater influence on planting decisions in major food-producing areas. In terms of topographic heterogeneity, the less flat the terrain, the more growers would choose to grow cash crops. A complementary relationship between the two forms of costs is also suggested by this study’s confirmation that explicit and implicit land costs can positively reduce each other’s impact on cropping decisions. Additional mechanism research revealed that explicit and implicit land costs impact crop crop-planting selections. The explicit cost of land primarily affects the local land transfer market effect, whereas the implicit cost of land affects the land transfer service.

Sustainable agricultural economic growth emphasizes the improvement of agricultural technical efficiency. This paper examines the impact of the rural land transfer market on agricultural technical efficiency by constructing a theoretical framework of the impact mechanism of the rural land transfer market on agricultural technical efficiency. The data of rural land transfer market in 30 provinces from 2005 to 2020 in China were used in this paper, and the stochastic frontier analysis (SFA) and Tobit model equation linkage was applied. The results showed: (1) In terms of time differences, the rural land transfer market had a significant stimulating and involution effect on agricultural technical efficiency. In 2006–2015, the rural land transfer market had a significant stimulating effect on agricultural technical efficiency, and, in 2016–2020, the rural land transfer market entered the stage of involution, and the rural land transfer market had a involution effect on agricultural technical efficiency. (2) In terms of regional differences, the stimulating effect of the rural land transfer market on agricultural technical efficiency was mainly concentrated in the main grain producing areas, and the involution effect was mainly concentrated in the non-main grain producing areas. (3) The involution effect of the rural land transfer market exceeded the stimulating effect, which made the rural land transfer market have a inhibition effect on agricultural technical efficiency from a comprehensive view of the overall trend.

Accurate farmland market data plays a crucial role in decision-making for policymakers, investors and operators. We present a nationwide, parcel-level dataset of farmland rent in China, derived from eight survey waves conducted between 2021 and 2025 across 27 provinces. The dataset comprises 7,237 rigorously validated samples, covering 191 cities and 422 counties in major agricultural production regions. Each record contains georeferenced parcel attributes and detailed transaction information, including parcel size, rental price, contracting parties, contractual terms. Compared with existing sources, this dataset offers broader coverage, finer spatial resolution, and improved temporal continuity, thereby addressing long-standing limitations of farmland transfer data in China. No other publicly available dataset provides comparable scope, reliability, and detail. Its release will enhance transparency in farmland markets and enable robust analyses of spatial and temporal dynamics, cross-regional comparisons, and the effects of institutional arrangements on land transactions. The dataset provides a valuable empirical foundation for advancing research in agricultural economics, rural development, and land use policy.

Farmers' off-farm working choices have a profound impact on family land use behavior under the rapid urbanization in China. This paper studies the mechanism of off-farm employment affecting farmland allocation behavior and focuses on the role of risk. Based on the Chinese Family Panel Studies (CFPS) data, Probit and Tobit models were used to empirically test the impacts of off-farm employment on farmers' land renting behavior, and instrumental variables were applied to test the stability of the models. The results show that households who join off-farm employment are more likely to rent land out, and they are more willing to rent out land to relatives or neighbors in the oral, informal, rent-free or low-rent contracts. But the incentive to rent out land won't increase with households’ off-farm level enhancing. The existence of unemployment risk will prevent farmers from land renting out, especially for farmers who mainly work in off-farm sectors. Besides, with the development of agricultural productive services, the workplace no longer has an impact on land rental behavior. In general, addressing the risk of loss of land rights and unemployment faced by farmers will effectively promote the development of rural land leasing market.