Accelerating progress toward the Sustainable Development Goals (SDGs) requires a clear understanding of key causal relationships at both national and sub-national levels, which is crucial for identifying key impediments and opportunities to enhance policy coherence across sectors. However, current research on the causal interactions between SDGs and their indicators at sub-national level remains limited. This study first utilizes Multi-spatial Convergence Cross Mapping (MCCM) and network analysis methods to construct causal networks of SDGs and their indicators in China and its 31 provinces from 2000 to 2020. It analyzed the primary causal features of China's SDGs in terms of synergy and trade-off effects, as well as their spatial differences. The results show that, from 2000 to 2020, the causalities among the SDGs followed a 5:2 ratio between synergistic and trade-off effects, establishing a solid foundation for SDGs implementation. In 28 provinces, the main synergistic causality involved SDG4 and SDG17, and the bidirectional causality between them being the key causal feature in 18 provinces. The main trade-off causality across 13 provinces involved SDG12 and SDG15, indicating that trade-off between resource use, ecological protection and other SDGs remained a major challenge in achieving SDGs. Additionally, neighboring provinces exhibited similar causal loop characteristics, and prioritizing high-frequency indicators including SDG4.c.1, SDG17.8.1, SDG4.2.2, SDG9.c.1, SDG4.a.1, and SDG11.7.1 within synergistic loops were key for SDGs development. This study provides comprehensive insights into future development priorities of China and its administrative regions, offering valuable guidance for promoting policy coherence and achieving systematic coordination of the SDGs.

Agricultural mechanization’s carbon consequences remain contested. Using provincial panel data from 30 Chinese provinces (2000–2023), we combine benchmark regression, mediation, threshold, and spatial models to examine how mechanization affects agricultural carbon emission intensity. We find a robust inverted U-shaped relationship, with an inflection point at 0.428; approximately 61% of province-year observations have entered the emission-reduction phase, while provinces with lower mechanization levels (e.g., Yunnan and Guizhou) remain on the upward slope. Mechanization operates through two opposing pathways; higher fertilizer application intensity increases emissions, while greater fertilizer use efficiency reduces them, with the intensity pathway accounting for the larger share of the observed mediated association (68% of the total mediated effect). The emission-reduction effect only materializes once fertilizer use efficiency crosses a critical threshold (−0.626 in log terms); provinces below this level face the risk of carbon lock-in as mechanization expands. A grain-oriented shift in cropping structure and spatial spillovers constitute additional channels, with the latter accounting for over half of the total effect. These findings call for regionally differentiated policies; provinces with persistently low fertilizer use efficiency should build precision fertilization capacity before scaling up mechanization, while more advanced regions can draw on cross-regional machinery networks to spread low-carbon agricultural practices.

Green technological innovation serves as a vital driver for the green transformation and cleaner production of polluting enterprises. Existing studies have primarily focused on the impact of individual environmental regulatory policies on green technological innovation, largely overlooking the green innovation dividends stimulated by the synergy of environmental regulatory policies. This study employs a difference-in-differences model to examine the impact of policy synergy between environmental protection tax reform (EPTR) and climate-adaptive city pilot (CACP) on green technological innovation (GTI), using panel data from 1753 Chinese A-share enterprises from 2014 to 2024. The findings indicate that the policy synergy of the dual pilots (SDP) significantly enhances GTI, and its incentive effect is stronger than that of a single-policy pilot. Heterogeneity innovation effect analysis shows that SDP encourages enterprises to pursue substantive green technological innovation but has no significant impact on the strategic type. Mechanistic analysis indicates that SDP achieves its effects by increasing enterprises' R&D investment at the micro level and strengthening environmental law enforcement at the macro level. Heterogeneity analysis shows that the effects are more pronounced in large-scale and state-owned enterprises, as well as in regions with advanced financial development. These findings are crucial for China to promote GTI through the synergy of EPTR and CACP, and also provide valuable references for other developing economies.

Realizing the Sustainable Development Goals’ commitment to “leave no one behind” requires accessibility that responds to evolving human needs, yet prevailing accessibility assessments remain static and sector-specific, overlooking hierarchical need evolution and behavioral feedback. We develop a Needs-responsive Accessibility Framework that integrates Maslow’s hierarchy with the Sustainable Development Goals to assess accessibility across five need tiers and their coupling coordination degree in China (2000–2020). By incorporating migration data, the framework further reveals accessibility-migration interactions. Results show that national accessibility increased by 21.26 points, corresponding to a compound annual growth rate of 3.39%, with development priorities shifting from physiological to higher-order needs—echoing Maslow’s theory. Meanwhile, east-west disparities narrowed and coupling coordination degree improved from 0.41 (uncoordinated) to 0.71 (moderately coordinated), indicating a more balanced human-need system. Migration motives likewise evolved from material to self-actualization needs, whose contribution rose from 20% to 45% as higher-order opportunities improved. These findings establish a demand-oriented framework for designing more inclusive sustainable strategies, particularly in resource-constrained regions. Accessibility in China rose from 2000 to 2020, and east-west disparities narrowed, system coordination improved while migration motives shifted toward self-actualization needs, according to an analysis that uses a needs-responsive accessibility framework integrating SDGs, Maslow’s hierarchy, and migration data.

The neighbourhood is the smallest unit at which a city can be said to work, or to fail, for the people who live in it [...]

Green finance is often viewed as being linked to sustainable growth, yet its effects may be uneven across regions with different industrial legacies. This paper examines how green finance correlates with green total factor productivity (GTFP) in China, with a focus on the country's legacy industrial regions (broadly referred to as the "Rust Belt" in this paper), spanning Northeastern and Central China. Using a province-year panel for 30 mainland provinces over 2006-2023, we measure GTFP with a Slacks-Based Measure-Global Malmquist-Luenberger (SBM-GML) index that accounts for undesirable outputs. To reduce simultaneity concerns, we estimate two-way fixed-effects models and conduct robustness checks, including lag-based specifications; nevertheless, the observational design implies that the estimates should be interpreted as stable associations rather than definitive causal effects. We reveal a concerning stylized fact: despite rapid growth in green finance, GTFP in legacy industrial provinces exhibits a nonlinear pullback. More formally, we document pronounced regional heterogeneity: green finance is positively related to GTFP in eastern coastal provinces but negatively related to GTFP in central and northeastern legacy industrial provinces. Our findings are consistent with the theoretical prediction of an intertemporal mismatch in Schumpeterian creative destruction: standardized green-credit tightening coincides with tighter liquidity conditions for incumbent high-carbon sectors, while green entrants in these regions may scale up only gradually, leaving a temporary output and productivity "valley" during the transition. The results suggest that uniform green-finance policies may amplify transition risks in legacy industrial regions, motivating a shift from purely "green finance" toward complementary "transition finance" tools.

Enhancing Energy-Embedded Green Utilization Efficiency of Urban Land (E-GUEUL) is crucial for reconciling economic growth with carbon neutrality targets, with the Integration of the Digital–Real Economy (IDRE) emerging as a key driver. This study measures city-level E-GUEUL using the super-efficiency SBM–Malmquist index model. To rigorously identify the causal effect of IDRE on E-GUEUL and address potential model misspecification and high-dimensional confounding factors, a Double Machine Learning (DML) framework is employed. Findings reveal a robust and significant positive effect of IDRE on E-GUEUL, a conclusion that holds across a series of robustness checks and endogeneity controls. Heterogeneity analysis indicates that the efficiency enhancement is more pronounced in non-resource-based, digitally developed, and eastern or central cities. Mechanism analysis reveals that optimizing Energy Consumption Intensity acts as a short-term driver, while Green Technology Innovation and Environmental Regulation serve as long-term sustainers. Furthermore, moderating effects reveal that Marketization exerts a positive moderating influence. This study provides empirical evidence and policy insights for leveraging IDRE to advance green growth through tailored approaches.

The year 2030 marks a pivotal milestone for China, algining with both the completion of the United Nations 2030 Agenda for Sustainable Development and the country’s commitment to peak carbon emissions under the Paris Agreement. Given limited time and resources, harmonizing decarbonization targets with Sustainable Development Goals (SDGs) is a core governance challenge. This study proposes a framework integrating Multi-spatial Convergence Cross-Mapping (MCCM), network analysis, and Graph Convolutional Network (GCN)-based scenario forecasting. By incorporating causal effects, structural importance, and evolutionary potential, the framework quantitatively derives SDG priority orders across Shared Socioeconomic Pathways (SSPs) under China’s carbon peaking. The results show that SDG indicators 7.2.L, 3.d.1, 1.1.L, 3.8.1, and 9.5.1 are core cross-pathway levers, generating spillover effects that are 60.7 %–91.5 % greater than the system average. Targeted resource allocation to these indicators can catalyze SDG advancement. At the goal level, prioritizing SDG 7 as a key driver (with spillover effects 57.3 % above mean) while addressing the persistent underperformance of SDG 15 provides the backbone for achieving cross-pathway synergies. Implementation sequences are path-dependent. In SSP1, SDG 4 and SDG 17 are the foundational pillars for harmonizing climate commitments with SDGs. Under SSP3 and SSP5, structural imbalances elevate the urgency of SDG 10 by 38.7 % and 37.3 % relative to SSP1, increasing its strategic priority. In SSP2 and SSP4, SDG 9 becomes pivotal, driven by lagging progress (10.7 % and 17.3 % behind SSP1) and amplified spillover effects (15.2 % above average). This study provides evidence-based guidance for China to optimize policies and maximize dual-goal synergies across pathways, while offering transferable insights for aligning sustainability and climate action globally.

Assessing the impact of public data access (PDA) on fiscal transparency is an important reference for scientific governance. However, the existing literature rarely explores the effect of PDA on fiscal transparency in the digital era. Based on the panel data of 286 prefecture-level cities in China from 2013 to 2021, this paper uses a multi-period Differences-in-Differences (DID) model to explore whether PDA can improve fiscal transparency, using the government data platform online as a quasi-natural experiment. The findings indicate that PDA is conducive to improving fiscal transparency. Mechanism analysis reveals that PDA can improve fiscal transparency by increasing government accountability, satisfying public demands, and breaking down data information barriers. The impact of PDA on fiscal transparency varies across cities with different characteristics, and there is significant heterogeneity in the impact results in terms of city location, city size, and government pressure.

Amid global climate change and energy constraints, green building represents a critical pathway for the construction industry’s decarbonization, yet its market development mechanisms remain underexplored. This study constructs a tripartite evolutionary game model analyzing dynamic interactions among consumers, construction enterprises, and the government, proposing a “Technology–Reputation–Policy” synergistic framework. The results reveal that the green building market equilibrium depends on government subsidy probabilities, subsidy amounts, stakeholder benefits, and cost reduction. While incentives significantly impact consumer behavior, their influence on enterprises is limited due to rapid strategic evolution. Government subsidy decisions balance reputational gains against expenditures, with market stability maintainable during subsidy reduction when technology-driven cost decreases reach threshold levels. Empirical calibration using Shenzhen data suggests a phased strategy: initial consumer subsidy prioritization, followed by technology cost-reduction alliances with gradual enterprise subsidy phase-outs, culminating in consumer subsidy reduction to ensure market self-sustainability. This study aims to explore “why” subsidy mechanisms effectively drive sustainable construction practices and the interaction mechanism among consumers, enterprises, and the government. These findings provide theoretical foundations and actionable policies for advancing green building markets under China’s dual carbon goals.

This study evaluates the spatial–temporal evolution of land use intensity and regional development under five shared socioeconomic pathways (SSPs) through prefecture-level projections in China (2020–2050). This study integrates the population–development–environment model with back propagation (BP) neural networks, a supervised learning algorithm, to analyze how differentiated development trajectories reshape land systems. Results reveal distinct pathways: SSP5 (conventional development) and SSP1 (sustainability) achieve high-income thresholds by 2025/2028 with intensive land development, while SSP3 (fragmentation) risks stagnation post-2037 accompanied by inefficient land use. Spatial analysis identifies persistent dualism across the Hu Huanyong Line—83.6% of urban land expansion concentrates in eastern regions, whereas western areas exhibit 56% lower land productivity. By 2050, regional land use efficiency differentials (0.3–4.3% Gross Domestic Product/capita growth) highlight challenges in balancing urban agglomeration and ecological conservation. These findings provide empirical evidence for optimizing land allocation policies during China’s economic transition.

The underlying tension between national park development and local community interests presents a significant challenge for contemporary ecological governance. Resident empowerment (RE) is increasingly recognized as a crucial pathway to mitigate this tension and achieve effective national park governance (NPG). However, the intrinsic mechanisms through which RE influences NPG have not been thoroughly explored in existing research. Drawing on the practice of government–resident interaction in China’s national parks, this paper investigates how the decentralization of power can balance the dual goals of environmental protection and social development. Using Three-River-Source National Park as a case study, we employ an ordered Logit regression model to examine the impact of RE on NPG. The study finds that RE is significantly and positively associated with NPG. Its influence is primarily mediated through three mechanisms: an identity effect (enhancing community belonging), an income effect (improving livelihood capabilities), and an environmental effect (strengthening participation in and perception of ecological conservation). Based on this empirical analysis, we recommend policies that further expand residents’ decision-making and management rights and broaden participation channels, thereby promoting the sustainable development and social equity of NPG.

To address air pollution and advance clean energy adoption, China’s “coal-to-electricity“ policy has encountered varied compliance among farmers due to income disparities. Integrating the Theory of Planned Behavior with income stratification, this study examines rural households’ behavioral intentions in Pu County, Shanxi, using structural equation modeling on survey data from 221 households. Results show distinct drivers across income groups: low-income farmers rely heavily on perceived behavioral control (β = 0.396, p < 0.01), emphasizing financial constraints; middle-income farmers balance policy trust and environmental awareness; and high-income farmers respond strongly to subjective norms (β = 0.760, p < 0.01), reflecting social influence. These findings argue against a uniform subsidy approach and propose tailored strategies—direct financial support for low-income groups, technical incentives for middle-income farmers, and normative interventions for high-income adopters—offering behaviorally-informed policy insights for advancing SDG 7 and SDG 13 in developing countries.

Urban rail transport is an important part of transport infrastructure, which is significant in empowering green-oriented urban development. However, few studies in the existing literature explore the effect of subway opening on urban green development. This paper uses Chinese data of 281 prefecture-level cities from 2008 to 2021 to explore the influence and mechanism of subway opening on urban green total factor productivity (GTFP) through the difference-in-differences (DID) and double dual machine learning (DDML) methods. The results show that subway opening can improve urban GTFP, and realize urban green and innovative development. This conclusion is supported by several robustness tests. With regard to pathways, subway opening can broaden the frontiers of production possibilities and promote technological progress by upgrading industrial structure and improving innovation capacity, thus increasing urban GTFP. Moreover, the effect of subway opening on urban GTFP is heterogeneous in city size, city location and regional resource endowment. Considering the heterogeneity difference of GTFP, it is found that subway opening has a stronger "green" effect in cities with high level of green development. Digital infrastructure and regional competition play a positive role in regulating the effect of subway opening on urban GTFP. Economic growth target has a strong constraint on the impact of urban GTFP by subway opening, while the constraint effect of the environmental target is not significant. These findings help to provide valuable empirical evidence for urban green development from a transport-related perspective.

Global climate change poses a significant challenge to achieving sustainable economic and social development goals. In pursuing carbon reduction goals, cities play an indispensable role, and enhancing their carbon efficiency is of critical significance. This study is designed to present fresh empirical evidence on urban low-carbon transition through the lens of public data openness. By adopting the launch of government data platforms as a quasinatural experiment, it constructs a multi-period differences-in-differences (DID) model. The research uses data from 282 Chinese prefecture-level cities spanning from 2010 to 2021 to thoroughly explore the impact and mechanisms of public data access (PDA) on urban carbon emission efficiency. The study found that: (1) PDA significantly enhances urban carbon emission efficiency, thereby facilitating urban low-carbon transition. The implementation of PDA has led to an average increase of 6.78 % in urban carbon emission efficiency. (2) Based on the "technology-structure-configuration" framework, "technology" denotes the adoption of low-carbon technology, "structure" refers to the shift from high-carbon to low-carbon industries, and "configuration" captures the reduction of factor misallocation, technological upgrading, structural upgrading and resource allocation optimization are effective mechanisms for PDA to promote urban carbon emission efficiency. (3) There are city cluster effects and resource endowment differences in the effect of PDA on carbon emission efficiency. (4) Extended analysis further highlights the positive moderating role of digital infrastructure development and urban business environment in the impact of PDA on urban carbon emission efficiency.

The transition to sustainable livelihoods is essential to enhancing rural households’ incomes and ecological conservation and to promote rural development. Previous studies have primarily focused on internal determinants of livelihood transformation, such as household livelihood capital. However, few studies have quantitatively analyzed the role of governmental interventions in livelihood transformation. Using an improved Sustainable Livelihoods Approach, we examined farmers’ perceptions of public services as an indicator of governmental influence in Horqin Left Wing Rear Banner in Inner Mongolia, a typical agropastoral transition zone. We examined the role of governmental interventions on farmers’ livelihood transformation decisions, applying entropy weighting and regression analysis to survey data gathered from 491 agropastoral households. We found that farmers’ positive perceptions of public services significantly influenced their willingness to transform their livelihoods, with every unit increase in positive perceptions associated with a 2.26-unit increase in transformation willingness. Perceptions of public services differed significantly among different types of farming households, with farming-only and mixed-farming households reporting more positive perceptions than herding-only and non-farming households. Furthermore, willingness to transform livelihoods varied significantly by household type, with mixed-farming households and herding-only households exhibiting greater willingness (average scores of 3.94 and 3.75, respectively) than farming-only and non-farming households (2.41 and 2.61, respectively). Lastly, interactions among public services and livelihood capitals significantly enhanced farmers’ willingness to transform their livelihoods. We recommend differentiated public service policies tailored to household characteristics and wide dissemination of improved agropastoral technologies and animal disease control services to promote a shift to sustainable livelihoods among rural households.

Urban sustainability has become the most important urban development issue globally. Facing the problem of spatial structure optimization during urbanization, how to effectively use public data access to promote urban polycentric development has become a new area of concern for urban planners and policy makers. To quantify how government open-data platforms shape polycentric urban spatial structure across Chinese cities, this study takes the launch of government data platforms as a quasi-natural experiment, constructs the multi-period differences-in-differences model, uses data of 271 Chinese prefectural-level cities from 2010 to 2021, and examines the impact and mechanism of public data access on urban spatial structure. We find that public data access promotes urban polycentric development, especially in large cities, those in urban agglomerations, and resource-abundant cities. The effect follows an inverted ‘N’ trend, which reflects the evolving role of PDA across different urban development stages, highlighting the need for adaptive policies to optimize its benefits. Mechanisms include information process radicalization and industrial structure upgrading, moderated positively by government intervention and regional competition. These insights can inform policies for optimizing urban spatial patterns and advancing sustainable urban development.

The mechanism for realizing the value of ecological products is a critical issue bridging the global Sustainable Development Goals (SDGs) and China's ecological civilization construction. Addressing a key methodological gap between the international TEEB framework (based on natural capital accounting and marginal utility pricing) and China's Gross Ecosystem Product (GEP) accounting, this study employs concept movement theory to deconstruct the dynamic evolutionary path of ecological products from "natural gifts" to "factors of production." The results reveal an interactive five-stage mechanism of "stipulation-unfolding-transformation-perfection-realization." Through policy text and case analysis, we find that the relativity of concept movement is reflected in the dynamic adjustment of policy tools, while technological innovation reduces transaction costs by reconstructing property rights boundaries. This study proposes that China's practice is centered on "institutionally embedded innovation," which localizes the natural capital paradigm through the labor theory of value. This forms a "global consensus-local innovation" dual-drive framework, providing a practical pathway for developing countries to overcome the challenges of measurement, transaction, and monetization.

As a global process, urbanization profoundly influences the achievement of the Sustainable Development Goals (SDGs). However, identifying how the trade-offs and synergies among the SDGs change at different stages of urbanization remains an urgent research challenge. This study focuses on underdeveloped mountainous areas, selecting Lincang City—China’s Sustainable Development Agenda Innovation Demonstration Zone—as a representative case. Based on multi-source data, this study employs Social Network Analysis (SNA) and Generalized Additive Model (GAM) to explore the impact of urbanization on the trade-offs and synergies among the SDGs. The results showed that: (1) Urbanization exerted a dual effect on SDG relationships, fostered synergies among certain goals (e.g., SDG3, SDG6, and SDG17), and transformed some synergies into trade-offs (e.g., SDG7 and SDG13). (2) The coupling among the SDGs displayed a dynamic pattern of “initial strengthening followed by weakening” as urbanization advanced. (3) SDGs exhibited varying clustering patterns at different stages of urbanization, shifted from three clusters at lower levels to two at middle levels, and eventually formed a new coupling pattern at higher levels. This study constructs and applies a localized SDGs indicator system to reveal the nonlinear evolution of interactions among the SDGs during the urbanization process, offering a novel perspective for understanding their dynamic coupling in the development of mountainous cities. It also provides scientific support for optimizing synergy pathways and advancing sustainable development in underdeveloped mountainous areas.

National innovation capacity (NIC) is key to achieving sustainable development. However, research on the link between NIC and sustainability remains limited. Based on data from 131 countries spanning 2015-2024, this study uses structural equation modeling to explore the impact mechanisms and dynamic characteristics of NIC's effects on the Sustainable Development Goals (SDGs). This paper specifically analyzes the pathways of NIC and its constituent elements (institutional, knowledge, and technological innovation capacity) in relation to SDGs under different target dimensions (social, economic, and environmental sustainability). The results show NIC promotes directly to SDGs with a contribution value of 0.286; however, the effects on economic, social and environmental SDGs vary significantly, with contribution values of 0.348, 0.103 and 0.193, respectively. Institutional, knowledge, and technological innovation capacities have indirect effects on SDGs through their interactions. Heterogeneity analyses reveal these effects are heterogeneous across countries according to geographic location, population density, income level, and economic development stage. Sensitivity analyses show enhancing each unit of NIC improves SDGs 9, 8, 7 and 11 by 0.727, 0.665, 0.624 and 0.536, respectively. SDGs 8, 9, and 11 are the most sensitive to institutional and technological innovation changes, while SDGs 5, 14, and 15 are insensitive to these innovation elements. Further analysis suggests NIC can enhance the collaborative achievement of the SDGs by mitigating trade-offs and amplifying synergies. Moreover, institutional innovation most effectively promotes synergies, while knowledge and technological innovation are more effective at alleviating trade-offs. The findings can inform both theoretical research and policy making for sustainable development.