In recent years, the synergy between new energy development and agricultural systems has become a major topic of global academic interest. Mainstream studies indicate that the application of new energy sources, especially solar, wind, and biomass, can not only enhance energy efficiency in agricultural production, but also promote the green transformation of agriculture and reduce carbon emissions (Haberl et al., 2011; Popp et al., 2014). Haberl et al. (2011) systematically reviewed the complex interactions among land use, energy crops, and food production, highlighting the profound impact of new energy utilization models on agricultural ecology and productivity. Popp et al. (2014) empirically demonstrated the important role of biomass energy in rural energy transitions, carbon emissions reduction, and enhancing agricultural resilience. Recent studies further show that new energy influences agricultural systems not only through energy substitution, but also via the electrification of irrigation, stabilization of production processes, and integration with land-use systems such as agrivoltaics. These findings suggest that the impact of new energy on agriculture should be evaluated from a systemic productivity-quality perspective rather than solely through output or emission indicators.

In existing studies, new energy development is mostly measured by indicators such as installed capacity, electricity generation, or investment efficiency (Chang et al., 2020; Xu et al., 2024), while agricultural productivity has gradually evolved from traditional yield or total factor productivity to green total factor productivity (AGTFP), encompassing environmental, resource, and technological innovation dimensions (Coomes et al., 2019; Chang et al., 2020. In addition, green finance and policy incentives have been proven as important drivers for improving Grain Productivity Quality Index and promoting the application of new energy (Chi, 2022). However, energy poverty is believed to significantly reduce agricultural productivity (Shi et al., 2022). These multi-dimensional measurement approaches provide a solid foundation for research into the synergistic mechanisms between new energy and the grain system.

Research on the mechanisms between new energy and agriculture is mainly divided into two categories: one emphasizes the crowding-out effect of new energy development on high-quality arable land, i.e., competition in land resource allocation (Haberl, 2015); the other focuses on technological spillovers and green synergies brought by new energy, such as the improvement in land use efficiency through agrivoltaic projects (Omer et al., 2022). Haberl (2015) discussed the dual pressures of new energy project siting on food security and ecological protection, warning of potential negative externalities. Omer et al. (2022) empirically revealed that agrivoltaic projects can increase land economic output, reduce carbon emissions, and promote ecological protection, serving as an innovative agricultural model driven by new energy.

Supporting literature shows an increasing focus on the link between energy structures and agricultural productivity. For example, Peng et al. (2024) construct an index of agricultural green productivity using panel data for 30 Chinese provinces, highlighting the spatial–temporal characteristics of agricultural green productivity and the influence of energy structure on its performance. Wang and Qian (2024) emphasizes the relationship between agricultural productivity and the pursuit of a just energy transition in China, pointing to the need for integrated approaches that align energy transition goals with agricultural development and sustainability. Li et al. (2022) find that both renewable energy consumption and demographic transitions have significant long-run impacts on agricultural green total factor productivity across 30 provinces, suggesting that energy use patterns shape productivity outcomes in rural China. Other provincial-level studies relate energy poverty and agricultural technical efficiency, showing that energy constraints can hinder improvements in agricultural productivity under certain conditions. Together, these studies underline that energy structure and energy use dynamics are important determinants of agricultural productivity at the provincial level, and they motivate further investigation into how new energy development interacts with multi-dimensional measures of productivity quality such as GPQI.

Since 2013, new energy projects such as agrivoltaics have rapidly proliferated in China, driving the green transformation of agricultural systems. Relevant studies have focused on policy promotion, regional disparities, and innovative mechanisms. For example, Xinhua News Agency (2024) reported the central government’s introduction of the Grain Productivity Quality Index concept, marking a new stage in China’s food security strategy. Furthermore, data from the National Energy Administration indicate that by 2023, China’s installed capacity of renewable energy surpassed 1.4 billion kilowatts, reflecting the deep integration of new energy and food security under policy-driven initiatives.

In summary, existing domestic and international studies have preliminarily revealed the interactive mechanisms between new energy development and agricultural production. However, further advancements are needed in variable characterization, mechanism identification, and system modeling. Future research should broaden multi-dimensional evaluation indicators, refine heterogeneity analysis, and apply frontier methods such as policy simulation and causal inference to systematically assess the boundary and practical pathways of new energy’s impact on Grain Productivity Quality Index. This will not only provide a theoretical basis for the synergy between China’s dual carbon and food security strategies, but also contribute valuable Chinese experience to the global transition toward sustainable agriculture.

We compile a provincial panel for 30 Chinese provinces, 2013–2022. The outcome variable is GPQI. The key explanatory variable measures new-energy development (clean-energy structure and deployment consistent with national statistical practice), and the control vector includes economic structure, market performance, agricultural inputs, and finance-related variables. All monetary variables are deflated to constant prices (base year specified in the data appendix) and log-transformed where appropriate to reduce skewness. All variables are constructed using publicly available data from the China Statistical Yearbook, China Rural Statistical Yearbook, China Energy Statistical Yearbook, and official provincial statistical bulletins. Detailed variable definitions and data sources are summarized in Tables 1, 2.

To examine the impact of new energy development on the Grain Productivity Quality Index (GPQI), The baseline regression model is specified as Equation 1 below.

In Equation 1 β 0 is the constant term, β 1 is the main regression coefficient, β 2 ~ β 5 represent the coefficients associated with the control variables, Province fixed effects and year fixed effects are included in the specifications, ε _i,t as the error term.

Table 3 presents the descriptive statistics of the variables, providing a detailed analysis of seven key variables. Overall, the means of the variables indicate a moderate level of development within the sample; however, the standard deviations and ranges reveal significant internal variation, indicating substantial regional disparities. Specifically, the mean values of Grain Productivity Quality Index (0.324) and the level of industrialization (0.312) are both in the moderate range, and their relatively low standard deviations (0.087 and 0.077, respectively) suggest that these indicators are fairly stable. In contrast, the standard deviations for new energy generation (mean = 0.307, SD = 0.388) and informatization level (mean 0.073, SD 0.153) are relatively large, with extremely low minimum values (as low as 0.004 for new energy generation and 0.015 for informatization) and high maximum values (5.447 and 2.520, respectively). This implies regional imbalance, with some sample points exhibiting weak performance in new energy or information technology application, while others perform exceptionally well. The mean for environmental regulation intensity is extremely low (0.003), but the range is wide (from 0.00006 to 0.031), indicating generally weak environmental policy intensity, though a few regions enforce stricter regulation. In addition, the mean proportion of elderly population is 16.33%, reflecting an overall trend of population aging, but its standard deviation (4.396) and range (from 8.75 to 28.77%) highlight significant differences in population structure. Overall, the data reveal development imbalances, especially in new energy, informatization, and demographic structure, suggesting that targeted policies may be necessary to narrow regional gaps.

Table 4 displays the results of the baseline regression examining the relationship between new energy development and Grain Productivity Quality Index. Column (1) shows that, in the absence of control variables, the coefficient for the primary explanatory variable (GPQI) is significantly positive at the 5% significance level. When control variables are introduced in column (2), the significance of GPQI increases, reaching the 1% level, and the adjusted R² also rises, reflecting a better model fit. Overall, these results indicate that the advancement of new energy development has a clear positive effect on Grain Productivity Quality Index.

To further mitigate potential endogeneity issues in the empirical analysis, this study applies a difference-in-differences (DID) framework to reassess the relationship between new energy development and Grain Productivity Quality Index. In December 2016, the National Energy Administration released the “13th Five-Year Plan for Energy Development” prioritizing the creation of clean energy demonstration provinces and regions as a central initiative for advancing the energy consumption revolution, and identifying several provinces as key pilots. Each selected province subsequently formulated detailed implementation strategies consistent with the national policy agenda. These pilots have focused on building clean, low-carbon, safe, and efficient energy systems, fostering the transformation of growth drivers, and supporting high-quality economic development. Rather than relying on traditional instrumental variables, this study addresses endogeneity concerns by exploiting the staggered implementation of the clean-energy demonstration province policy as a quasi-exogenous shock within a multi-period DID framework.

Within this context, the clean energy demonstration province policy is treated as an external shock to evaluate its influence on Grain Productivity Quality Index. This approach is justified as all types of new energy considered in this study—hydropower, wind, solar, and nuclear—are classified as clean energy, and related policies are expected to shape their adoption and expansion. The exogeneity of the policy’s rollout makes it suitable for addressing endogeneity challenges in the model.

To measure the policy’s impact, a multi-period DID strategy is implemented, treating the policy’s introduction as a quasi-natural experiment. The DID method reduces selection bias and helps alleviate endogeneity, while the multi-period approach accommodates policy effects that unfold over time. This methodology statistically estimates the average treatment effect by leveraging all possible combinations of periods and treatment groups, allowing for a more nuanced and accurate assessment of the economic impacts associated with staggered policy implementation.

In operationalizing this approach, the actual year of policy implementation in each pilot province is used as the intervention point, dividing the sample into treatment and control groups to compare shifts in Grain Productivity Quality Index before and after the policy. Based on official records, the designated pilot provinces and their respective years of implementation are: Ningxia (2012), Zhejiang (2014), Sichuan (2016), Tibet (2016), Gansu (2016), and Qinghai (2018). These six provinces comprise the treatment group, with the remaining 25 provinces serving as the control. Due to extensive missing data, Tibet is excluded from the analysis, resulting in a final treatment group of five provinces. The model structure is outlined in Equation (2).

In Formula 2 Policy it is a dummy variable indicating whether province i has implemented the clean energy demonstration province policy in year t; it takes the value of 1 if and only if the pilot province i is in the year of policy implementation or any year thereafter, and 0 otherwise.

In columns (1) and (2) of Table 6, Policy it coefficients are all significantly positive, These results demonstrate that the clean energy demonstration province policy exerts a significant positive effect on Grain Productivity Quality Index, regardless of the inclusion of control variables. Since Ningxia implemented the policy in 2012, but the study period starts in 2013, Ningxia is removed from the sample to avoid potential bias, and the regression is conducted again. As presented in column (3) of Table 7, both the magnitude and significance of the coefficients remain consistent. Overall, the findings from the multi-period DID approach align with the primary regression outcomes, further verifying the robustness and reliability of the main results.

Considering that the validity of the multi-period DID model relies on the parallel trends assumption, Figure 1 presents the coefficient dynamics of the parallel trends test for the impact of new energy development on Grain Productivity Quality Index. Here, “current” represents the year of policy implementation, while “pre 5” “pre 4” “pre 3” “pre 2” and “pre 1” denote five, four, three, two, and 1 year(s) before policy implementation, respectively. Similarly, “las 1” “las 2” “las 3” “las 4” “las 5” “las 6” “las 7” and “las 8” refer to one to 8 years after policy implementation.

The parallel trends test is conducted by selecting “pre_1” as the baseline period. According to the results in Figure 1, the treatment effect coefficients for “current” and subsequent years are all significantly greater than zero, while the 95% confidence intervals for the coefficients prior to “current” mostly include zero, indicating that the parallel trends assumption holds. In summary, the multi-period DID model satisfies the parallel trends condition, confirming that new energy development has a significant positive effect on Grain Productivity Quality Index.

To further ensure the unbiasedness of the estimated coefficients in the multi-period DID model, a placebo test is conducted in this study. The Bootstrap method is used to simulate random group assignments between the treatment and control groups. As shown in Figure 2, the circles represent the kernel density distribution of the estimated coefficients from the Bootstrap randomization experiments, while the dashed line indicates the estimated coefficient from the multi-period DID model. It can be seen from Figure 2 that the estimated coefficient of the multi-period DID model in this study is unbiased.

To further analyze the heterogeneous effects of new energy, the explanatory variable is disaggregated, and the development levels of hydropower, wind power, solar power, and nuclear power in each province are measured by the proportion of each type’s electricity generation in total energy generation. The impact of each energy type on Grain Productivity Quality Index is then analyzed, with regression results shown in Table 7. First, as indicated in columns (1) and (2), hydropower (Water) and wind power (Wind) do not have significant effects on Grain Productivity Quality Index. For hydropower, this is primarily because resource distribution does not align with major grain-producing areas, and hydropower cannot be directly utilized at agricultural production sites. Hydropower stations are typically located far from major grain-producing regions, with electricity fed into the national grid through centralized, large-scale generation, making it difficult to serve agricultural production directly. In the case of wind power, wind farms are concentrated in coastal and plateau areas rich in wind resources, while major grain-producing regions lack such resources. The high installation and maintenance costs, as well as the technical threshold for wind power, make self-installation unfeasible in most cases. Therefore, both hydropower and wind power exert only indirect influence on Grain Productivity Quality Index, and their direct effects are not significant. The insignificant effects of hydropower and wind power may be attributable to their technological and spatial characteristics. These energy sources are typically deployed in geographically concentrated locations and depend heavily on large-scale grid integration, which may weaken their direct linkage with local agricultural production systems. In addition, hydropower and wind resources are often located in regions with limited arable land or lower grain-production intensity, reducing their capacity to directly support on-farm electrification and productivity-enhancing technologies. By contrast, solar energy can be deployed in a more decentralized manner and integrated with agricultural land use, while nuclear energy provides stable low-carbon baseload power that better supports electrification across production, processing, and logistics (Hu, 2023).

In contrast, as shown in columns (3) and (4) of Table 7, solar power (Sun) and nuclear power (Nuclear) exhibit significant positive effects on Grain Productivity Quality Index. Solar power provides stable electricity for agricultural production through photovoltaic generation, reducing energy costs. Particularly in remote areas, photovoltaic power is not dependent on the grid and can be directly applied to agricultural production, supplying energy for irrigation and electric farm machinery, thus realizing “zero distance” between energy production and consumption. Moreover, integrated agrivoltaic projects improve land use efficiency and extend the agricultural industry chain. Nuclear power, with its high efficiency, stability, and clean electricity generation, offers reliable power for grain processing and cold-chain logistics, reducing production interruptions and losses. Nuclear energy can also provide high-temperature process heat and clean electricity for zero-carbon hydrogen production, significantly reducing carbon emissions in the fertilizer industry. In addition, technologies such as nuclear irradiation breeding and soil improvement accelerate crop variety improvement, enhance soil quality, and improve pest and disease control, further strengthening the sustainability of grain production.

First, the findings demonstrate that new-energy development exerts a significant positive influence on the Grain Productivity Quality Index (GPQI). Quantitatively, a 1% rise in new-energy development corresponds to an average increase of 0.41% in GPQI (p < 0.01).

Second, this positive effect remains robust across multiple verification strategies, including alternative definitions of the explanatory variable, omission of municipality samples, and incorporation of additional control variables. The results also hold when addressing potential endogeneity using the clean-energy demonstration-province policy within a multi-period DID framework.

Third, the heterogeneity analysis by energy type shows that solar and nuclear meaningfully enhance GPQI, whereas hydropower and wind do not display statistically significant effects.

Fourth, additional heterogeneity analyses indicate that the influence of new-energy development varies across spatial, policy, economic, and financial contexts. In particular, a pronounced positive effect is found in major grain-producing areas, eastern regions, the latter half of the sample period (approximately 2018–2022), provinces with strong fiscal support for agriculture, and regions characterized by higher levels of marketization and lower levels of financial development. These findings deepen the understanding of energy–agriculture linkages by demonstrating how energy transition can enhance the quality dimension of grain productivity, and they provide empirical support for aligning China’s dual-carbon goals with food security and agricultural modernization strategies.

1. Coordinate region-specific new-energy deployment to foster energy–agriculture synergy and raise GPQI.

Based on the empirical findings and heterogeneity analyses, the following policy recommendations are proposed to promote coordinated development between new-energy transition and grain productivity quality across regions. Regions should formulate scientific new-energy strategies aligned with local resource endowments, industrial bases, and ecological carrying capacities. In economically developed eastern provinces, it is recommended to accelerate the deployment of efficient clean energy sources, particularly solar and nuclear energy, thereby enhancing agricultural performance through energy-structure optimization. In central and western regions as well as resource based areas, wind and hydropower should be developed in accordance with local conditions, while the expansion of agrivoltaics and agroforestry and solar integration should be promoted to enhance land use efficiency and grain production capacity. In addition, local governments are encouraged to establish demonstration zones and innovation platforms for deep integration between the energy and agricultural sectors, thereby facilitating coordinated regional upgrading of both systems.

2. Strengthen green technological innovation in agriculture and promote adoption of clean-energy equipment to enhance GPQI’s multidimensional content.

Increase public investment in green-agriculture R&D and agricultural clean-energy equipment, with targeted support for breakthroughs in the electrification and digitalization of production. Encourage industry–academia–research collaboration to accelerate demonstration and diffusion of technologies such as solar-powered irrigation and biogas utilization. Improve technology-transfer and extension mechanisms to involve innovative firms and cooperatives in green-equipment development, thereby raising the technological content, sustainability, and resilience captured by GPQI.

3. Tighten land and ecological governance to ensure coordinated development of agriculture and new energy without compromising grain security.

While vigorously advancing new-energy projects, strictly protect arable land and the ecological environment to safeguard the baseline of grain security. Establish clear land-use standards and ecological-compensation rules for new-energy siting; promote eco-friendly compound models (e.g., agrivoltaics, agroforestry–solar) and encourage multiple-use land arrangements. Strengthen dynamic supervision of facility layout and operations, and enhance ecological restoration and green-compensation efforts to prevent disorderly land competition and ecological degradation stemming from rapid new-energy expansion. This approach supports a virtuous interaction among energy transition, grain security, and ecological protection Power (2010).

4. Policy Recommendations with Provincial Differentiation.

The policy implications of this study vary across provinces with different development conditions. In major grain-producing regions and provinces with relatively strong land governance capacity, policies should prioritize the deep integration of new energy with agricultural production, such as promoting agrivoltaic systems, electrified irrigation, and energy-efficient processing facilities. In economically developed eastern provinces, where energy infrastructure is more advanced, efforts should focus on leveraging clean and stable energy sources to support agricultural upgrading along the entire value chain. By contrast, in less developed regions and provinces with weaker fiscal capacity, policy support should emphasize basic energy access, targeted subsidies, and institutional coordination to ensure that new-energy deployment effectively translates into improvements in grain productivity quality rather than exacerbating land-use conflicts.

Despite its contributions, this study has several limitations that warrant further investigation. First, the analysis is conducted at the provincial level, which effectively captures regional aggregate characteristics but may mask intra-provincial heterogeneity in land-use patterns, agricultural practices, and new-energy deployment. Second, the sample period from 2013 to 2022 primarily reflects a specific stage of new-energy development and does not explicitly distinguish dynamic differences across development phases. Third, as the empirical analysis is based on macro-level data, the micro-level mechanisms through which new-energy development affects grain productivity quality remain to be further explored using finer-scale data. In addition, this study does not explicitly model potential spatial spillover effects or policy interaction mechanisms between energy transition and agricultural systems. Future research could address these limitations by employing more disaggregated data, extending the time horizon, and incorporating spatial or multi-policy analytical frameworks to deepen understanding of the coordinated evolution between new-energy development and grain production systems.