Search Results (997)

Improving digital service performance in the public sector has become central to modernizing national governance and to advancing the United Nations’ Sustainable Development Goal (SDG) 16—effective, accountable institutions. Empirical research on digital government services remains limited, particularly on how different policy instruments shape service performance. Drawing on policy mix theory, this study introduces the concept of a bounded policy mix (BPM), referring to a policy configuration in which data governance, process re-engineering, and standardization operate within defined boundaries. It examines the effect of this three-dimensional configuration on digital government service performance (DGSP). Using panel data from 296 prefecture-level cities in China from 2018 to 2024, this study employs a city–year two-way fixed-effects model. The findings reveal that the bounded policy mix is significantly and positively associated with DGSP under both original performance measures and resource-adjusted measures. Mechanism analysis suggests that process re-engineering helps connect data governance with standardization and translate these instruments into service performance. The findings are consistent with a degree of functional complementarity among the three policy instruments. Adding platform construction to the core combination does not significantly strengthen the positive effect. These findings provide empirical evidence for understanding the performance effects of digital government policy mixes. Full article

Digital transformation has become an important pathway for promoting sustainable urban governance. As cities increasingly adopt data-driven governance approaches, public data opening, defined in this study as the launch of local public data opening platforms and the platform-based release and reuse of digital public data by local governments, provides new opportunities to improve resource allocation efficiency and support more sustainable urban development. This study examines whether and how public data opening contributes to sustainable urban governance by reducing urban land resource misallocation in China. Based on the phased establishment of local public data opening platforms across Chinese cities, this study constructs a panel dataset of 285 prefecture-level cities for the period 2007–2022. It then examines the policy effect using a multi-period difference-in-differences model, a threshold regression model, and a Spatial Durbin Model. The results show that public data opening significantly reduces urban land resource misallocation, with the baseline estimate suggesting an economically meaningful reduction in land allocation distortion. The main conclusion remains stable after a range of validity tests and robustness checks. Mechanism analysis indicates that platform-based public data opening improves land allocation by enhancing local governance capacity, strengthening government transparency, and promoting market integration. Heterogeneity analysis further shows that the governance effect is stronger in cities with higher land finance dependence, more industry-oriented economic structures, and higher levels of land marketization. Further analysis suggests that digital infrastructure may serve as an enabling condition for the effectiveness of platform-based public data opening. The spatial analysis shows that the effect of public data opening exhibits certain inter-city associations, although the decomposed indirect effects do not provide consistent evidence of robust spatial spillovers. These findings highlight platform-based public data opening as an important digital governance instrument for strengthening sustainable urban governance and improving the efficiency of urban resource allocation. Full article

Digital industrial cluster development increasingly depends on the coordinated allocation of capital, data, technology, and innovation resources. This study examines whether and how government-guided venture capital funds and the openness and utilization of high-value public data jointly promote digital industrial cluster development. Using a balanced panel of 285 Chinese prefecture-level cities from 2014 to 2023, we estimated two-way fixed-effects models and conducted mechanism, moderation, heterogeneity, robustness, and endogeneity analyses. The results revealed significant complementarity between government-guided venture capital funds and high-value public data in promoting digital industrial cluster development, and this finding remained robust to alternative specifications, measurements, and endogeneity treatments. Dimension-specific analysis showed that the effect is concentrated mainly in the contribution and support dimensions. Innovation factor agglomeration and enhanced collaborative innovation both partially mediated the effect, with the former playing a larger role. Economic development and population density strengthened the effect of capital–data synergy, whereas government intervention weakened it. Further heterogeneity analysis showed that the synergy effect is stronger in southern cities and becomes more pronounced over the sample period, while its marginal contribution declines as pre-existing regional innovation capacity increases. These findings extend research on policy complementarity and coordinated factor allocation and provide empirical evidence for designing differentiated capital–data policies to support sustainable digital industrial cluster development. Full article

This study utilizes Sentinel imaging to monitor small farming plots (<5 ha) and objectively estimate their significance in agricultural output. It develops, tests, and evaluates methodologies within and across ten southern European NUTS-3 regions—commonly referred to as prefectures, provinces, or departments—located in Greece, Italy, France, Portugal, and Spain. The study provides stakeholders with the necessary tools to estimate their spatial distribution, crop diversity, crop area extent, and agricultural yields. The approach is expanded as needed to similar landscapes, serving as a model for other European NUTS-3 regions and providing a comprehensive view of the remote sensing solution. It employs random forest crop identification and classification analysis to build crop-type maps and record the countless small agricultural plots. The Sentinel data utilized are from 20 NUTS-3 pilot regions selected across 11 EU countries, focusing on 29 estimated key crop areas. The research demonstrates remarkable classification accuracy for a previously identified wide range of key crop data across each of the selected NUTS-3 regions. Crop area estimates derived from field data were adjusted and stratified. The RS-based methodology reduces propagation errors in estimating the area and production of crop categories with low accuracy levels, enhancing the overall understanding of the crucial role of small farming plots. For highly accurate key crop products (per crop-type categorization, with $FScore$ > 75%), production estimates are calculated by multiplying predicted self-reported crop yields by unbiased key crop small farming plot area estimates. However, for the referenced period of cultivation, only 16 out of the above 29 estimated key crop areas are recorded for SFs by the regional official statistics. In addition, of the 16 reported key crop areas listed above, eleven are from nine NUTS-3 regions spread across four southern EU countries, with the remaining five coming from four NUTS-3 regions of two non-southern EU countries. The analysis shows a strong correlation between estimated key crop areas from SFs reported in regional official statistics and Sentinel-based key crop area estimates obtained from small farming plots, with $R^2=0.96$ for 16 estimated key crop area datasets across all NUTS-3 regions in the EU and $R^2=0.98$ for 11 estimated key crop area datasets across all southern NUTS-3 regions. As a result, in NUTS-3 regions lacking regional official statistics data, RS can serve as a reliable alternative source for estimating the extent of key crop areas. Full article

Against the backdrop of the coordinated advancement of the dual carbon goals and new-type urbanization, the traditional high-carbon development model has become a major constraint on urban green transformation. As a critical vehicle for digital technologies to empower low-carbon governance, smart cities have yet to receive a systematic evaluation of their long-term policy effects based on quasi-natural experiments. Using panel data from 280 prefecture-level cities from 2003 to 2023, this study takes the smart city pilot policy as a quasi-natural experiment. It adopts Interpretive Structural Modeling (ISM) to identify the key influencing factors and transmission paths of carbon emissions, and employs the progressive difference-in-differences (DID) model to assess the carbon emission reduction effects, dynamic evolutionary characteristics and urban heterogeneity of smart city construction. Furthermore, the mediation effect model is applied to clarify its underlying mechanisms. The empirical results show that smart city construction significantly curbs urban carbon emissions, and this finding remains valid after a series of robustness tests, including the parallel trend test, placebo test and PSM-DID. The emission reduction effect of the policy exhibits an obvious time lag: the effect is insignificant in the first and second years after policy implementation but turns significantly negative and continues to strengthen starting from the third year. Noticeable urban heterogeneity is also observed, with a more prominent emission reduction effect in eastern regions, central cities with high administrative ranks and large-sized cities. Mechanism analysis reveals that the conventional industrial pollution reduction pathway does not serve as the primary transmission channel. Instead, a suppression effect is identified, suggesting that smart cities achieve carbon abatement primarily through the digital empowerment of energy allocation efficiency—a pathway distinct from traditional end-of-pipe governance approaches. Unlike previous studies, this study combines ISM with a staggered DID framework to reveal the dynamic effects and transmission mechanisms of smart city policies. Full article

As industrial green transformation advances and urban risks become more complex, this study explores the relationship between China’s national green industrial park policy and urban resilience. The empirical analysis draws on panel data for 293 Chinese cities at the prefecture level or above between 2011 and 2024 and uses a Double/Debiased Machine Learning (DML) framework to flexibly account for observed city characteristics and possible nonlinear relationships. A positive association is observed between the green industrial park policy and the composite urban resilience measure, with this pattern remaining broadly consistent under a range of robustness and sensitivity analyses. Positive coefficients are also obtained across all four resilience components; however, the empirical support is comparatively stronger for the ecological and infrastructure components than for their economic and social counterparts. The pathway results are compatible with potential roles for environmental governance investment, residents’ income, and new-quality productive forces. Differences in the estimated relationships are more pronounced when cities are grouped by resource dependence and industrial-base characteristics, whereas the east–central–west classification does not yield statistically significant between-group variation. By shifting attention from park-specific environmental performance to broader resilience outcomes at the city level, this study extends the scope of green industrial park policy assessment. Given the assumptions underlying the empirical design, the reported coefficients represent conditional relationships rather than conclusive causal evidence. Full article

Amid China’s pursuit of ecological civilization and high-quality development, a central policy challenge lies in designing environmental governance instruments that can simultaneously deliver ecological improvement and socially inclusive outcomes. Using the National Forest City (NFC) program as a representative urban ecological governance initiative, this study examines the relationship between place-based environmental certification and urban inclusive green growth (IGG) in China. Drawing on panel data from 290 prefecture-level cities over the 2000–2023 period, this study exploits the staggered rollout of the NFC program and estimates its relationship with urban inclusive green growth using a multi-period difference-in-differences framework. The results show that NFC designation is associated with a statistically significant increase in cities’ IGG performance, and this relationship remains robust across a series of alternative specifications and placebo tests. Dimension-specific estimates further show significant positive associations with economic performance, social equity, and environmental quality. Further analyses reveal pronounced heterogeneity in the estimated relationship. The positive association between NFC designation and inclusive green growth is stronger in non-resource-based cities, cities located in southern China, and core urban areas, suggesting that local development conditions and structural constraints play an important role in shaping the observed relationship. Employment-related analysis provides evidence consistent with broader city-level employment expansion as one potential pathway associated with the NFC–IGG relationship. Moreover, the positive association between NFC designation and IGG is stronger in cities with greater population mobility and where local governments place greater emphasis on innovation, underscoring the importance of complementary institutional and demographic conditions. By explicitly linking ecological governance, labor-market outcomes, and inclusive growth, this study contributes to the policy-oriented literature on urban sustainability. The findings suggest that environmental certification programs such as the NFC may provide a governance framework for connecting ecological objectives with inclusive development goals. Beyond the Chinese context, the results offer transferable insights for cities seeking to design environmental governance strategies that move beyond symbolic greening toward more integrated and socially inclusive sustainability transitions. Full article

Background/Objectives: Healthcare resources are unevenly distributed across Miyazaki Prefecture, Japan. We examined regional differences in the clinical presentation of renal cell carcinoma (RCC) recorded in the Miyazaki Urological Cancer Database (MUCD), together with area-level healthcare resource indicators. Methods: We performed a retrospective descriptive observational analysis of prospectively collected data from the multicenter MUCD registry. After deduplication and rechecking eligibility, 540 patients with a documented diagnosis date within the study period (1 April 2019–31 March 2024) were included (Central/Southern, n = 333; Western, n = 116; Northern, n = 91). The three main clinical outcomes were adjusted for age and sex, with clustering by registering facility taken into account. Results: Presentation with predefined local symptoms occurred in 18.6%, 19.0%, and 34.1% of patients in the Central/Southern, Western, and Northern areas, respectively. Compared with Central/Southern, the adjusted odds ratio for predefined local symptoms in the Northern area was 2.30 (95% CI, 1.39–3.81). Median tumor size was 3.8, 4.4, and 5.0 cm, respectively; the adjusted mean difference for Northern versus Central/Southern was 1.24 cm (95% CI, 0.65–1.82). Clinical M1 disease was recorded in 10.2%, 12.1%, and 19.8%, respectively; the global test accounting for facility clustering yielded p = 0.076, and the Holm-adjusted p value for Northern versus Central/Southern was 0.082. Urologist density in 2024 was 8.7, 8.0, and 5.3 per 100,000 population, respectively. Conclusions: Regional differences were observed in the clinical presentation of MUCD-recorded RCC and in area-level healthcare resource availability. These findings are descriptive and hypothesis-generating and do not show that differences in resource availability caused the observed clinical patterns. Full article

Against the backdrop of global forest degradation and timber supply constraints, improving green production efficiency in forestry while securing timber supply is an important challenge for sustainable forest management. Treating China’s National Reserve Forest Policy (NRFP) as a quasi-natural experiment, this study uses panel data for 114 prefecture-level cities from 2011 to 2022, including 67 treated and 47 control cities. We employ a staggered difference-in-differences estimator based on group-time average treatment effects, together with transmission-channel analysis, difference-in-difference-in-differences (DDD), and a spatial difference-in-differences–spatial Durbin model (Spatial DID-SDM). The baseline estimates yield an overall ATT of 0.0149, indicating an average increase of 0.0149 index points in forestry GTFP for treated cities relative to the estimated counterfactual without the NRFP. This effect is approximately 1.49% of the sample mean of forestry GTFP (0.998) and remains stable across multiple robustness and sensitivity checks. NRFP implementation is separately associated with increases in capital deepening (CD) and physical labor productivity (PLP), while the lagged values of CD and PLP are each positively associated with forestry GTFP; these results are consistent with CD and PLP as potential transmission channels. The estimated NRFP effect is significantly weaker in resource-based cities and significantly stronger in economy-efficiency-oriented regions. Spatial effect decomposition shows that both the direct and indirect effects of the NRFP are positive and statistically significant. Under the alternative spatial weight matrix, both effects retain their direction and statistical significance, while the magnitude of the indirect effect remains broadly stable, suggesting that the positive spatial spillover effect of the NRFP on forestry GTFP is reasonably robust to the spatial weight matrix specification. These findings provide city-level empirical evidence for evaluating NRFP performance and informing differentiated policy design. Full article

Developing new quality productive forces (NQPF) is an important direction for accelerating China’s high-quality development and advancing Chinese-style modernization. Based on the connotation and characteristics of NQPF, this study constructs an urban evaluation index system from three aspects of niche width, height, and overlap, and uses panel data of 283 Chinese prefecture-level cities over 2010–2023 to examine the spatiotemporal evolution patterns and key influencing factors of urban NQPF. Results are as follows. Firstly, urban NQPF exhibits an overall upward trajectory with an average annual growth rate of about 4.89%, showing phased features of rapid growth, moderated growth, and slight decline, and a spatial gradient of Eastern leadership, Central catch-up, and relatively low levels in the Western and Northeastern zones, with high-level cities growing into the largest group. Secondly, niche width, height, and overlap all show long-term upward trends. The energy level of factor resources and external resource acquisition capacity grow persistently. Inter-city competition evolves from moderate to high intensity, and regional disparities gradually converge. Thirdly, niche width and height are significantly and positively associated with NQPF development, whereas overlap is significantly and negatively associated, with marked regional heterogeneity. Combinatorial pathway analysis reveals that dominant pathways shift from “LLL + MLM” to “HHH + MMH” types, with HHH dominating high-level cities, MMH prevailing among medium-level ones, and low-level cities shifting from LLL to LLH. Improving energy level of urban new qualitative factor resources, strengthening spatial interaction level of new qualitative factors among cities, and avoiding adverse effects caused by excessive competition among cities should become an important content of optimizing the spatial layout of NQPF factor resources in China. Full article

Achieving low-carbon agriculture in major grain-producing areas hinges on balancing emission reduction imperatives with the need to maintain food security. However, previous studies have largely examined agricultural carbon emissions (ACE) at administrative scales, potentially overlooking heterogeneous agricultural functions and emission pathways within urban agglomerations. This study integrates ACE accounting, spatial inequality and autocorrelation analyses, and Logarithmic Mean Divisia Index (LMDI) decomposition to investigate the spatiotemporal dynamics, spatial differentiation, and driving mechanisms of ACE in the Main Grain-Producing Areas of the Middle Reaches of the Yangtze River, China, using panel data from 31 prefecture-level cities during 2013–2023. The results show that ACE experienced expansion, decline, and subsequent stabilization, while agricultural carbon intensity decreased by 37.29%, indicating gradual decoupling between agricultural development and carbon emissions. Significant spatial heterogeneity and positive spatial dependence were observed, with intra-agglomeration disparities contributing more to overall inequality than inter-agglomeration differences. The three urban agglomerations also exhibited distinct emission characteristics associated with their agricultural functions and emission structures. LMDI decomposition showed that agricultural economic development was the main factor increasing ACE, whereas improvements in production efficiency and changes in rural population mitigated emissions. Overall, the results reveal that similar regional emission trends can arise from heterogeneous agricultural functions, emission structures, and development trajectories. This highlights the need for differentiated low-carbon transition pathways rather than uniform emission-reduction strategies and provides a basis for coordinating food security with low-carbon agricultural development. Full article

Coordinated regional development of agricultural and rural modernization is essential to remedy sectoral shortcomings and to advance comprehensive rural revitalization. Panel data from 16 cities in Shandong Province (2014–2023) were analyzed using GMI and LMI models to study spatio-temporal evolution and spatial correlation in three economic circles. The Dagum coefficient decomposed regional disparities, while an obstacle degree model identified key hindering factors. The findings were as follows. Agricultural and rural modernization in Shandong has followed a fluctuating but rapid growth trajectory, with a clear spatial hierarchy: Jiaodong > Lunan > Provincial Capital. Spatial agglomeration types HH and LL occurred mainly in Jiaodong and Lunan, respectively. By contrast, spatial outliers of type HL included Dezhou, Jinan and Jining, while LH characterized Rizhao and Dongying. Overall discrepancies in agricultural and rural modernization within the province arose mainly from differences between these economic circles and, on the whole, were converging. Although Provincial Capital has experienced episodic widening of intra-circle disparities, differences within Jiaodong and Lunan have tended to narrow. Persistent constraints on agricultural and rural modernization in Shandong Province included comprehensive control level of soil and water loss, governance expenditure on rural communities, industrialization level of agricultural products, and Agro-product Geographical Indications, while the comprehensive control level of soil and water loss was also the common obstacle factors across three major economic circles, and industrialization level of agricultural products became common to all three from 2020 onward. Each circle should exploit its particular resource endowment and development base. To narrow development gaps among prefecture-level cities, it is essential to clarify functional roles, strengthen inter-city division of labor and cooperation, and promote coordinated regional development of agricultural and rural modernization across the economic circles. Full article

High-resolution measurements of near-surface optical turbulence over the Tibetan Plateau are essential for optical propagation studies, astronomical site characterization, and adaptive optics applications. In this study, a multi-level tethered UAV system was deployed in Ngari Prefecture, China, to obtain synchronous in situ observations of atmospheric optical turbulence from 10 to 190 m above ground. Using measurements at 10–90 m as inputs, we developed a physics-informed reconstruction framework to estimate $C_n^2$ profiles at 110–190 m, thereby approximately doubling the vertical range covered relative to the directly used observations. The framework decomposes the turbulence structure into an equilibrium component describing the large-scale vertical profile and a residual component representing departures from equilibrium. The equilibrium structure is modeled using a dynamically fitted power-law relationship, while a residual-learning module captures short-term variability associated with evolving thermal and dynamical processes. On the independent test period from the same field campaign, the reconstructed profiles achieve an average RMSE below 0.6 in $\lg \left(C_n^2\right)$ and an average $R^2$ above 0.75, outperforming empirical extrapolation and representative data-driven baselines. The proposed framework provides reliable estimates of upper-layer optical turbulence under daytime and transition-period conditions over the Ngari Plateau. Full article

Background/Objectives: Regional HPV genotype data for the Yangtze River Delta remain scarce, limiting evidence-based vaccine and screening policies. This study characterised the HPV genotype distribution, the vaccine protection gaps, and the co-infection network structure among clinical women in Wuxi, a prefecture-level city in the Yangtze River Delta region of Jiangsu Province, China. Methods: We retrospectively analysed all 8515 cervical HPV genotyping records from Xishan People’s Hospital of Wuxi City (January 2024–June 2025) using a 23-type fluorescent PCR panel classified by the International Agency for Research on Cancer (IARC) criteria. Results: The overall HPV prevalence was 22.83% (1944/8515; 95% CI 21.9–23.8%). The five leading types were HPV52 (4.82%), HPV58 (3.14%), HPV16 (2.96%), HPV53 (2.50%), and HPV42 (2.08%); HPV18 (0.74%) ranked below two unvaccinated carcinogens, HPV51 (1.76%, IARC Group 1) and HPV68 (1.68%, Group 2A). The age-stratified prevalence was U-shaped (χ² = 123.27, p < 0.001), peaking at ≤20 years (52.94%; exploratory, n = 51) and ≥61 years (34.66%). Among the 1944 women with positive results, 43.0% were completely unprotected by the nonavalent vaccine, and 60.4% harboured at least one non-9vHPV type; HPV51 and HPV68 together accounted for 15.08% of the positive cases. In the co-infection network (n = 585 multiple-type infections), HPV52 achieved the highest degree centrality (281 co-infection events; normalised degree = 0.0218) and appeared in 8 of the 15 most frequent dual-type pairs, with observed/expected ratios of 6.5–14.8-fold. Conclusions: These findings reveal a clinically substantial vaccine protection gap and support the prioritisation of HPV51 and HPV68 in next-generation vaccine design. Full article

In the context of the global implementation of the United Nations 2030 Sustainable Development Goals (SDGs), urban green development serves as a core practical pathway to advance SDG8 (Decent Work and Economic Growth), SDG11 (Sustainable Cities and Communities), and SDG15 (Life on Land). This study constructed a three-dimensional evaluation framework encompassing the green economy, green ecology, and green society, and utilized panel data from 282 Chinese prefecture-level cities from 2009 to 2021. By integrating the CRITIC weighting method, spatial autocorrelation, the Standard Deviational Ellipse (SDE) approach, and the Dagum Gini decomposition, we systematically quantified multi-scale spatiotemporal evolution and disparity patterns at the national scale, as well as across four major economic zones and five core urban agglomerations. Nationwide, the composite green development index rose from 0.422 to 0.522 (+23.70%), while the overall Dagum Gini coefficient declined from 0.072 to 0.053 (−26.39%), with the spatial center of gravity gradually shifting toward the southwest. Eastern China persistently maintained the highest development level; Western China achieved the fastest cumulative growth (26.43%), and Northeastern China lagged behind with the lowest growth rate (20.10%). Among five major urban agglomerations, the Yangtze River Delta (YRD) and Pearl River Delta (PRD) shared an identical average score of 0.582, whereas the Chengdu–Chongqing (CC) agglomeration recorded the highest growth of 33.07%. This research contributes multi-scale empirical evidence for localized SDG implementation in China and provides differentiated policy insights for cross-regional green transition governance. Full article