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In response to global climate change, low-carbon transition in the industrial sector has become essential for emission reduction. Industrial parks, as concentrated centers of production, are major sources of urban energy use and carbon emissions. Whether park-based policy interventions can generate broader decarbonization effects remains unclear. This study conceptualizes China’s National Low-Carbon Industrial Park Pilot Policy (NLCIPP) as a meso-level systemic intervention and examines its impact on urban carbon intensity (UCI). Using panel data for 282 Chinese cities from 2006 to 2020, causal effects are identified through a multi-period DID framework combined with a synthetic DID approach. The results show that the NLCIPP significantly reduces UCI, indicating that energy-oriented interventions at the industrial park level can induce broader decarbonization outcomes. The policy effect mainly works via reduced energy consumption and enhanced green technological capability, while the contribution of industrial structural upgrading is relatively limited. Stronger impacts appear in central regions, cities with stricter environmental regulation, and non-resource-based cities, highlighting the context-dependent effectiveness of energy transition policies. These findings provide empirical evidence for designing effective industrial energy policies to promote low-carbon transition.

Unmanned aerial vehicle (UAV) small object detection faces critical challenges including irreversible geometric detail loss during multi-level downsampling, cross-scale feature distortion from interpolation blur and aliasing, and limited long-range dependency modeling due to constrained receptive fields. To address these limitations, we propose HMF-DEIM (High-Fidelity Multi-Domain Fusion Transformer for UAV Small Object Detection), an end-to-end architecture tailored for UAV small object detection. First, we design a lightweight hierarchical differentiation backbone that removes redundant deepest-layer features (P5) to prevent tiny object information loss, employing Multi-Domain Feature Blending (MDFB) in shallow layers for geometric detail preservation and a Hierarchical Attention-guided Feature Modulation Block (HAFMB) in deep layers for global semantic modeling. Second, we develop a full-chain high-fidelity feature transformation framework comprising Channel-Adaptive Shift Upsampling (CASU) for interpolation-free resolution recovery, Multi-scale Context Alignment Fusion (MCAF) for bridging deep–shallow semantic gaps via bidirectional gating, and Diversified Residual Frequency-aware Downsampling (DRFD) for aliasing suppression through a three-branch parallel architecture. Finally, we devise the FocusFeature module that aligns multi-scale features to a unified scale and employs parallel multi-scale large-kernel depthwise convolutions to capture cross-scale long-range dependencies, generating dual-scale (P3/P4) features balancing details and semantics. Experiments demonstrate that HMF-DEIM outperforms DEIM on VisDrone2019 test by 0.405 mAP50 (+2.1%) and 0.235 mAP50–95 (+1.6%), with a remarkable 21.3% relative improvement in APs for tiny objects, while maintaining real-time inference (465 FPS with TensorRT FP16) on an NVIDIA A100 GPU with only 11.87M parameters and 34.1 GFLOPs. Further validation on AI-TOD v2 and DOTA v1.5 datasets confirms robust generalization across diverse aerial scenarios, making it a practical solution for resource-constrained UAV applications.

This study investigates how digital transformation influences corporate greenwashing and promotes genuine sustainable development, drawing on the framework of fraud risk factors. Based on panel data from Chinese publicly listed companies between 2009 and 2022, a two-way fixed effects model is employed, with endogeneity addressed through difference-in-differences and instrumental variable techniques. The results show that digital transformation significantly curbs greenwashing by mitigating motivations, reducing opportunities, and enhancing exposure. The effect is stronger for growth- or mature-stage enterprises, non-myopic firms, and regions with low regulatory intensity and high environmental awareness. Furthermore, a double-threshold effect is identified, with the inhibitory role of digital transformation becoming more significant at intermediate and advanced stages. Importantly, digital transformation reduces greenwashing without compromising firms’ financial or sustainable performance. These results provide actionable insights for businesses and policymakers in curbing greenwashing and advancing sustainable development.

Near-infrared-I/II fluorescent proteins (NIR-I/II FPs) are crucial for in vivo imaging, yet the current NIR-I/II FPs face challenges including scarcity, the requirement for chromophore maturation, and limited emission wavelengths (typically < 800 nm). Here, we utilize synthetic protein-seeking NIR-II dyes as chromophores, which covalently bind to tag proteins (e.g., human serum albumin, HSA) through a site-specific nucleophilic substitution reaction, thereby creating proof-of-concept biomimetic NIR-II FPs. This chemogenic protein-seeking strategy can be accomplished under gentle physiological conditions without catalysis. Proteomics analysis identifies specific binding site (Cys 477 on DIII). NIR-II FPs significantly enhance chromophore brightness and photostability, while improving biocompatibility, allowing for high-performance NIR-II lymphography and angiography. This strategy is universal and applicable in creating a wide range of spectrally separated NIR-I/II FPs for real-time visualization of multiple biological events. Overall, this straightforward biomimetic approach holds the potential to transform fluorescent protein-based bioimaging and enables in-situ albumin targeting to create NIR-I/II FPs for deep-tissue imaging in live organisms. Near-infrared-I/II fluorescent proteins (NIR-I/II FPs) are crucial for in vivo imaging, but their availability is still scarce. Here, the authors make use of protein-seeking NIR-II dyes as chromophores, which covalently bind to tag proteins and thus creating biomimetic NIR-II FPs.

In recent years, many locales featuring therapeutic landscapes have seen a rise in health tourism. Existing scholarship tends to either concentrate on specific types of landscape or analyze human emotional experiences separately, often overlooking how therapeutic landscape experiences arise from interactions among human and non-human actors. This study focuses on the relationship between tourists and non-human actors (plants such as rice and lotus leaves, etc.) through immersive interaction. This research is built on critical plant theory and draws on a case study of Xixiang Rural Garden, Erlang Town, China, to examine the co-evolution of therapeutic landscape experience and health tourism and its inherent dynamism. Utilizing qualitative methods, data were collected between October 2024 and September 2025 through participatory observation, semi-structured interviews, and policy document analysis, involving diverse stakeholders, including local government officials, project designers, villagers, and tourists. From a micro-level empirical perspective, the study examines the co-evolution of therapeutic landscape experiences and health tourism and its underlying dynamics. The results show that visitors’ therapeutic experiences deepen through a cyclical process of “therapeutic spatial practices–relational negotiations–experiential transformation.” Key mechanisms driving this process include plant agency, cross-cultural dialogue, and multisensory engagement, which collectively facilitate the transition from initial sensory perceptions to deeper ecological awareness and multispecies relations. Based on micro-level empirical analysis, this study offers concrete policy insights for local governments seeking to promote the sustainable development of therapeutic tourism. In response to practical challenges, specific pathways are proposed: constructing plant-led symbiotic environments, establishing multisensory activity mechanisms, and adopting community-driven management models. These recommendations provide practical guidance for enhancing therapeutic landscape experiences and promoting the sustainable advancement of rural health tourism.

This study examines if peers’ digital transformation affects focal firms’ greenwashing, addressing the literature gap of insufficient focus on industry interactions via institutional theory. Using a sample of Chinese listed companies, the paper conducts an empirical analysis and finds that the digital transformation of peer enterprises significantly inhibits the greenwashing behavior of focal enterprises. This inhibitory effect is realized through three key mechanisms: the competitive peer spillover effect of digital transformation, the suppression of peer spillover in greenwashing behavior, and the convergence effect of industry-wide information disclosure quality. Moreover, this inhibitory effect is particularly pronounced in industries characterized by low short-termism tendencies, high technology intensity, high pollution levels, and fierce competition. Further research confirms that the initial emergence of highly digitalized enterprises in an industry triggers a “catfish effect,” and once the proportion of digitalized enterprises exceeds 50%, the inhibitory effect on greenwashing behavior becomes significantly stronger.

With the nationwide completion of China’s large-scale Poverty Alleviation Relocation (PAR) initiative in 2020, the government’s poverty alleviation efforts have officially entered the “post-poverty era”. However, many regions still lack well-established sustainable development mechanisms and face a potential risk of returning to poverty. To better stabilize the achievements of poverty alleviation, this study examines the potential risk of returning to poverty after the first Five-Year Transition Period (2021–2025) from a livelihood space perspective and proposes optimization directions for PAR policies in future poverty reduction efforts. Research findings indicate that simply altering geographical conditions is insufficient to achieve stable poverty alleviation. The production space of relocated populations is vulnerable to the stability and precision in resource supply, which may lead to recurring poverty due to policy discontinuities and administrative preferences. Meanwhile, improvements in living spaces are constrained by imbalances in household income and expenditure. This study also found that, on the one hand, changes in residential patterns break the original boundaries of administrative villages by incorporating migrants from different villages into concentrated communities, leading to the expansion of weak-tie networks while, on the other hand, the relocation process disrupts some of the migrants’ original strong-tie networks, and the concentration and clustering of impoverished groups in relocation communities further lead to the contraction of these networks. Additionally, the unique characteristics of relocation communities generate exorbitant governance costs and population management difficulties that far exceed the service provision and administrative capacities of community organizations. In the long run, this situation proves detrimental to normalized community governance and dynamic poverty relapse monitoring and interventions. Accordingly, this study proposes relevant policy recommendations from the following four aspects, i.e., strengthening endogenous development capacity, improving social security mechanisms, expanding social support networks, and enhancing organizational governance capabilities, aiming to provide both a theoretical basis and a decision-making reference for future poverty alleviation efforts.

Against the backdrop of the global sustainable development agenda and deepening reforms of China’s state-owned enterprises (SOEs), the restrictive effect of policy burdens on the long-term development capacity of SOEs has become increasingly prominent. How to break this constraint through policy reforms has become critical. This study takes China’s policy on the transfer of heating, power, water supply, and estate in the residential quarters of SOE employees (HPWET) as a quasi-natural experiment. Employing data from 2012 to 2024 on Chinese A-share SOEs listed in Shanghai and Shenzhen, combined with the staggered difference-in-differences method, to explore the impact of removing policy burdens (RPB) on the ESG performance of SOEs and the underlying mechanisms. Results show that RPB significantly improves SOEs’ ESG performance, with an average increase of 14.2% in the ESG performance of SOEs in the treatment group. This effect is more pronounced in large SOEs, those in regions with higher levels of technology marketization, and SOEs in light-pollution industries. Mechanism tests indicate that the improvement of the green innovation level, the reduction in political connections, and the optimization of the corporate governance environment are the core paths of action. This study further broadens the research perspective on SOE policy burdens, enriches the understanding of macro-policy drivers of the ESG performance, and provides new empirical evidence for emerging economies to break through the bottleneck of ESG development in SOEs through institutional reforms.

During production, copper mining could generate substantial waste rock that impacts land use and the environment. Advances in deep learning have enabled efficient, cost-effective intelligent sorting, where vision sensor performance critically determines sorting accuracy and efficiency. However, the sorting environment of copper mine waste rock is inherently complex, particularly within the conveyor belt section of the sorting machine, where insufficient and uneven lighting significantly impairs the performance of vision-based detection systems. To address the above challenges, a deep-learning-based copper mine waste rock detection algorithm under low-light environments is proposed. Firstly, an Illumination Adaptive Transformer (IAT) module is added as a preprocessing layer at the beginning of the Backbone to enhance the brightness of the images acquired by the vision sensor. Secondly, a Local Enhancement-Global Modulation (LEGM) module is integrated after the A2C2f and C3k2 modules in the Neck to enhance the detection accuracy. Finally, to further improve the model performance, MPDIoU is introduced to optimize the original loss function CIoU. As a result, the proposed algorithm achieved an mAP@0.5 of 0.957 and an mAP@0.5:0.95 of 0.689, outperforming advanced methods by 1.9% and 8.6%, respectively.

Ensuring the rights of involuntary resettlers is fundamental to a law-based state and essential for achieving social equity and sustainable development. However, institutional improvement depends not only on the intent of top-level design but also on the capacity for dynamic adaptation amid evolving social contexts. Moving beyond the predominant research focus on policy design principles, this study investigates the dynamic evolution of China’s reservoir resettlement rights protection policies from 1949 to 2025. We first constructed a corpus of 32 core policy documents. Employing a bibliometric analysis within a multi-dimensional framework, we statically examined the developmental patterns of these policies. Subsequently, we applied the Punctuated Equilibrium Theory (PET) to dynamically analyze their policy changes, identifying a trajectory marked by both long-term stability and significant punctuations. Our findings reveal that over 76 years, the policy process has undergone two major equilibrium periods and two critical punctuation nodes, demonstrating a clear pattern of “protracted stability interspersed with short bursts of rapid transformation.” The policy image has correspondingly evolved through four distinct stages: “Administratively Mobilized Resettlement,” “Development-Oriented Resettlement,” “Harmonious Society for Resettlers,” and “Common Prosperity.” The study argues that this evolution is driven by the interplay of shifting central government attention, the occurrence of focusing events, and the reinforcement of evolving Policy Images, which collectively broadened the policy venue and led to non-linear institutional change. Based on these findings, the paper recommends: first, adopting a dynamic approach to policy formulation; second, maintaining sustained political commitment and robust institutional safeguards; and third, fostering multi-stakeholder consultation and collaborative governance mechanisms. These strategies are essential to more effectively secure the multifaceted rights of reservoir resettlers.