Search Results (8)

In the coordinated management of air pollutants and carbon emissions, governments impose differentiated regulatory requirements on gases, while mitigation technologies have heterogeneous abatement potential. However, existing studies on emission quota management, an important mitigation instrument, focus on single gases and neglect integrating multigas policy preferences and heterogeneous abatement potentials, weakening policy responsiveness and scheme feasibility. This study develops a two-stage allocation framework. First, policy preference weights are introduced to evaluate multigas synergistic emission reduction potential and determine maximum quota reduction constraints for each gas. Second, policy preference weights and a non-radial directional distance function (NDDF) are embedded in a zero-sum gains data envelopment analysis (ZSG-DEA) model to capture multigas heterogeneity in policy preferences and reduction constraints, improving applicability and feasibility. Applied to the coal-fired power sector, the results show that, relative to the equal weight scenario, CO₂ incentive intensity rises by 22% under a carbon priority scenario and SO₂ incentive intensity increases by 13% under a pollution priority scenario, while the maximum quota reduction ratios of CO₂ and SO₂ are constrained from 41.75% to 9.18% and from 78.57% to 37.28%, respectively, ensuring alignment with policy preferences and keeping abatement within feasible ranges to support carbon neutrality and pollution control targets, thereby contributing to sustainable development. Full article

Achieving synergies between government-led and market-based policy instruments is critical to advancing Energy–Environmental Productivity and Sustainable Urban Development. This study investigates the effects of China’s dual-pilot energy policies (New Energy Demonstration Cities (NEDCs) and Energy Consumption Permit Trading (ECPT)) on urban environmental productivity (UEP) across 279 prefecture-level cities from 2006 to 2023. Utilizing a Non-Radial Directional Distance Function (NDDF) approach, combined with Difference-in-Differences (DID) estimation and spatial econometric models, the analysis reveals that these synergistic policies significantly enhance both comprehensive and net measures of UEP. Mechanism analysis highlights the roles of industrial restructuring, technological innovation, and energy transition in driving these improvements, while heterogeneity analysis indicates varying effects across different city types. Spatial spillover analysis further demonstrates that policy impacts extend beyond targeted cities, contributing to broader regional gains in UEP. These findings offer important insights for the design of integrated energy and environmental policies and support progress toward key Sustainable Development Goals (SDG 7, SDG 11, and SDG 12). Full article

Under the dual constraints of rigid water resource management systems and China’s “dual carbon” national strategy, water resource management authorities face pressing practical demands for the coordinated governance of water conservation and carbon dioxide emission reduction. This study comprehensively compiles nationwide data on water supply/consumption, energy use, water intensity, and CO₂ emissions across Chinese provinces. Employing a non-radial directional distance function (NDDF) model with multiple inputs and outputs, we quantitatively assess provincial water saving and carbon reduction performance during 2000–2021; measure synergistic effects; and systematically examine the spatiotemporal evolution, correlation patterns, and convergence trends of three key indicators: standalone water saving performance, standalone carbon reduction performance, and their synergistic performance—essentially addressing whether “1 + 1 > 2” holds true. Furthermore, we analyze the spatial convergence and clustering characteristics of synergistic effect across regions, delving into the underlying causes of inter-regional disparities in water–carbon synergy. Key findings reveal the following: ① Temporally, standalone water saving and carbon reduction performance generally improved, though the water saving metrics initially declined before stabilizing into sustained growth, ultimately outpacing carbon reduction gains. Synergistic performance consistently surpassed standalone measures, with most regions demonstrating accelerating synergistic enhancement over time. Nationally, water–carbon synergy exhibited early volatile declines followed by steady growth, though the growth rate gradually decelerated. ② Spatially, high-value synergy clusters migrated from the western to eastern regions and the northern to southern zones before stabilizing geographically. The synergy effect demonstrates measurable convergence overall, yet with pronounced regional heterogeneity, manifesting a distinct “high southeast–low northwest” agglomeration pattern. Strategic interventions should prioritize water–carbon nexus domains, leverage spatial convergence trends and clustering intensities, and systematically unlock synergistic potential. Full article

While existing research has focused on logistics carbon emissions, understanding spatiotemporal emission cost dynamics and drivers remains limited. This study bridges three gaps through methodological advances: (1) Applying the Non-Radial Directional Distance Function (NDDF) to measure Marginal Carbon Abatement Costs (MCAC), overcoming traditional Data Envelopment Analysis (DEA) model’s proportional adjustment constraints for provincial heterogeneity; (2) Pioneering dual-dimensional MCAC analysis integrating temporal trends (2013–2022) with spatial autocorrelation; and (3) Developing a spatial Durbin error model with time-fixed effects capturing direct/indirect impacts of innovation and infrastructure. Based on provincial data from 2013–2022, our findings demonstrate a U-shaped temporal trajectory of MCAC with the index fluctuating between 0.3483 and 0.4655, alongside significant spatial heterogeneity following an Eastern > Central > Northeastern > Western pattern. The identification of persistent high-high/low-low clusters through local Moran’s I analysis provides new evidence of spatial dependence in emission reduction costs, with these polarized clusters consistently comprising 70% of Chinese cities throughout the study period. Notably, the spatial econometric results reveal that foreign investment and logistics infrastructure exert competitive spillover effects, paradoxically increasing neighboring regions’ MCAC, a previously undocumented phenomenon in sustainability literature. These methodological advancements and empirical insights establish a novel framework for spatial cost allocation in emission reduction planning. Full article

The low-carbon city pilot (LCCP) policy represents a pioneering approach to fostering sustainable development. It offers a scientific framework to reconcile the relationship between economic growth, resource utilization, and environmental protection. This study measures urban ecological efficiency (UEE) through the non-radial directional distance function (NDDF) model using the panel data of 284 cities in China, from 2007 to 2021, and analyzes the impact of the LCCP policy on UEE, adopting a multi-period difference-in-differences (DID) model. The results of the baseline regression indicate that the pilot cities exhibit an average ecological efficiency that is approximately 3.0% higher than that observed in non-pilot cities, which pass both the parallel trend test and the robustness test. Mechanism analysis reveals that industrial upgrading and energy consumption reduction are the primary pathways through which the LCCP policy enhances UEE. In addition, the policy effects are particularly significant in improving UEE in non-resource-based cities, large cities, and cities in the eastern region. Finally, the spatial spillover effects demonstrated by the LCCP policy can effectively inform neighboring cities of strategies to enhance their UEE. The research findings provide invaluable insight and direction for China’s efforts in the development of low-carbon cities and ecological sustainability. Full article

Clean energy is an essential means to limiting carbon emissions and improving economic transformation, and a market-oriented financial structure is the inevitable result of the deepening of supply-side financial reforms. Exploring whether clean energy enhances carbon emission efficiency (CEE) through financial structural adjustment is essential in formulating policies intended to achieve the dual goals of “carbon peaking” and “carbon neutrality”. As part of the evaluation of China’s provincial CEE using panel data of 30 provinces from 2000 to 2019, this paper adopts an improved nonradial directional distance function (NDDF), while empirically analyzing the influence of clean energy and a market-oriented financial structure on CEE using a spatial econometric model. The results indicate the following findings: (1) The provincial CEE in China is characterized by significant spatial autocorrelation. (2) A 1% increase in the integration of clean energy and a market-oriented financial structure leads to a 0.0032% increase in the local CEE and a 0.0076% increase in neighboring regions’ CEE through the spatial spillover effect. Clean energy can efficiently enhance CEE through the stock market, while it has a passive impact through bank credit. (3) The interactive effect between clean energy and a market-oriented financial structure varies according to the provincial CEE. From the 25th to the 90th quantiles, the role of clean energy in promoting CEE through the capital market is very significant, while clean energy inhibits CEE through bank credit in most provinces. Therefore, China’s clean energy development will bolster its competitiveness in the global market through a market-oriented financial structure that will bring economic development and environmental pollution into balance and provide a theoretical foundation for China’s double carbon reduction. Full article

This paper investigates the impact of two types of environmental regulations (ERs), command-and-control environmental regulation (CACER) and market-incentive environmental regulation (MIER), on green total factor productivity (GTFP) through outward foreign direct investment (OFDI) in 30 provinces in China for the period of 2006–2019. The Global Malmquist–Luenberger (GML) Index based on non-radial directional distance function (NDDF) considering undesired outputs is used to measure GTFP growth at the provincial level. To explore the mediating effect of OFDI, the two-step econometric model and the non-linear mediating effect model are employed. The empirical results show that CACER has an inverted U-shaped impact on OFDI and a U-shaped impact on GTFP, while MIER has a linearly positive effect on OFDI and GTFP. The current intensity of CACER lies on the left side of the inflection point of the U-shaped curve. OFDI significantly positively influences the increase in GTFP and is a significant mediating variable in the relationship between ERs and GTFP. Moreover, the introduction of OFDI delays the appearance of the inflection point. Further analysis, taking into account the regional heterogeneity, indicates that the inverted U-shaped and U-shaped curve is still valid in the eastern and western area and that the mediating effect of OFDI on ERs in the western area is stronger than that in the eastern area. Based on these conclusions, policy implications are provided to improve GTFP in China. Full article

To tackle the increasingly severe environmental challenges, including climate change, we should pay more attention to green growth (GG), a path to realize sustainability. Human capital (HC) has been considered a crucial driving factor for developing countries to move towards GG, but the impact and mechanisms for emerging economies to achieve GG need to be further discussed. To bridge this gap, this paper investigates the relation between HC and GG in theory and demonstration perspective. It constructs a systematic theoretical framework for their relationship. Then, it uses a data envelopment analysis (DEA) model based on the non-radial direction distance function (NDDF) to measure the GG performance of China’s 281 prefecture level cities from 2011 to 2019. Ultimately, it empirically tests the hypothesis by using econometric model and LightGBM machine learning (ML) algorithm. The empirical results indicate that: (1) There is a U-shaped relationship between China’s HC and GG. Green innovation and industrial upgrading are transmission channels in the process of HC affecting GG. (2) Given other factors affecting GG, HC and economic growth contribute equally to GG (17%), second only to city size (21%). (3) China’s HC’s impact on GG is regionally imbalanced and has city size heterogeneity. Full article