Search Results (318)

Artificial Intelligence (AI) has emerged as a transformative force in the global economy, yet its contribution to environmentally sustainable productivity growth remains insufficiently understood. Using 33,017 firm-year observations from 4079 Chinese A-share listed firms during 2015–2024, this study examines the relationship between AI adoption and corporate green total factor productivity (GTFP). We construct a text-based proxy for AI adoption by applying a machine learning-generated dictionary to the management discussion and analysis (MD&A) sections of annual reports. We construct the GTFP proxy using the slacks-based measure, the Malmquist–Luenberger (SBM-ML) index, which incorporates undesirable outputs. The results show a significant positive relationship between AI adoption and GTFP. This relationship remains robust across a series of robustness checks, including alternative specifications, PSM-matched sample analysis, instrumental variable estimation, and exogenous shock design. Further analysis identifies R&D intensity as an important transmission channel. The relationship is stronger among firms facing tighter financing constraints, non-polluting industries, and non-state-owned enterprises. The positive association between AI adoption and firm value further indicates that its economic relevance may extend beyond environmental efficiency in the long term. These findings highlight the potential of AI-enabled innovation to advance green productivity and sustainable corporate development in emerging economies. Full article

Against the backdrop of China’s Dual Carbon Initiative and national transportation empowerment strategy, accelerating the low-carbon green transition of the transport sector has emerged as an imperative developmental priority. Incorporating carbon emissions as undesirable outputs into the efficiency evaluation framework, this study adopts a multi-method analytical paradigm encompassing the super-efficiency SBM model, Malmquist–Luenberger index, kernel density estimation, Dagum Gini coefficient, geographical detector model, and Geographically and Temporally Weighted Regression (GTWR). Based on panel data covering 30 provincial administrative regions in China from 2004 to 2022, this paper systematically investigates the spatio-temporal evolutionary patterns and intrinsic driving mechanisms of carbon total factor productivity (CTFP) within the transportation industry. The main findings are as follows: (1) The static efficiency results reveal that the national mean CTFP is below unity, indicating overall inefficiency. Nevertheless, it exhibits a fluctuating upward trend after 2009. Regionally, CTFP follows this pattern: Eastern China > Central China > national mean > Northeastern China ≈ Western China. (2) Dynamic productivity analysis shows that the annual average ML index is close to 1, demonstrating an overall upward trend in CTFP, and productivity growth is primarily driven by technological progress. (3) In terms of spatio-temporal patterns, inter-regional disparities constitute the principal source of overall spatial gaps in CTFP, with considerable contribution from transvariation density. (4) Geographical detector analysis suggests that energy intensity (EI) and economic development level are the two factors most strongly correlated with the spatial differentiation of CTFP, and interaction effects exist between them. The results from geographically weighted regression (GWR) further confirm that the strength of the correlation between each driving factor and CTFP presents pronounced regional heterogeneity. Based on these findings, differentiated regional policies for emission reduction and efficiency improvement should be formulated in accordance with the law of diminishing marginal returns. Full article

Waste accumulation and land carrying pressure are common sustainability challenges in urbanization, making the improvement of urban land green use efficiency (ULGUE) a key issue. Using panel data from 278 Chinese cities over 2008–2023, this study treats zero-waste city pilots as a quasi-natural experiment and constructs a difference-in-differences model, measuring ULGUE via a super-efficiency slacks-based measure–Global Malmquist–Luenberger (Super-SBM-GML) model under variable returns to scale. The results show that zero-waste city construction significantly and robustly improves ULGUE. Mechanism analysis shows that zero-waste city construction improves ULGUE mainly by optimizing the waste treatment structure, promoting green technological innovation, and facilitating the development of green industries. Heterogeneity analysis shows that the promoting effect is stronger in eastern regions, key environmental protection cities, old industrial base cities, and resource-based cities. Moderating effect analysis indicates that rising public environmental attention and internet development strengthen this effect. Spatial econometric analysis further shows that zero-waste city construction generates significant positive spatial spillovers on neighboring regions in addition to improving local ULGUE. This study reveals the underlying logic and micro-level transmission paths of zero-waste city policy, situates China’s experience within global circular economy governance, and offers policy evidence for emerging economies advancing green land use. Full article

Improving water resource use efficiency (WRUE) is essential for achieving sustainable water management under increasing socioeconomic and environmental pressures. This study investigates the spatiotemporal evolution, associated factors, and spatial transition characteristics of WRUE across 11 provincial-level administrative regions in the Yangtze River Basin during 2010–2024. An integrated framework combining the super-efficiency SBM-window DEA model, Malmquist–Luenberger index, GeoDetector, and conventional and spatial Markov chain models was developed to characterize efficiency dynamics, productivity changes, explanatory factors, and state-transition pathways. The results showed that WRUE exhibited an overall fluctuating upward trend with a clear spatial gradient of lower reaches > middle reaches > upper reaches. The mean ML index was 1.004, indicating that technological change (TC) was the main contributor to productivity improvement. Urbanization rate, water use per CNY 10,000 of GDP, industrial water-use share, and primary-industry share exhibited relatively high explanatory power, and their interactions enhanced explanatory power. Markov analysis revealed strong persistence in WRUE states, while transition probabilities differed across spatial neighborhood conditions. Assuming stable transition probabilities, the high-efficiency state would reach a steady-state probability of 0.8155. These findings provide insights for differentiated water resource management and coordinated regional development. Full article

The logistics industry remains a critical bottleneck for decarbonization due to its extensive physical networks and high-carbon path dependencies. To address the spatial heterogeneity and transition constraints in the logistics industry of China, this study proposes a Performance–Topology–Mechanism (PTM) framework. Analyzing panel data from 30 Chinese provinces, we integrate a Super-SBM model with the Global Malmquist–Luenberger (GML) index, Dagum Gini decomposition, and a panel Tobit model to decode the spatiotemporal dynamics of green innovation performance (GIP) and its underlying spatial lock-in mechanisms. The results reveal a steady but spatially uneven increase in the national GIP (from 0.4526 to 0.5724), characterized by a leading East and a lagging West/Northeast. GML decomposition indicates this growth is predominantly driven by outward shifts in the technological frontier rather than efficiency improvements, highlighting the weak spatial conversion of green technologies into transport optimization. Topological tracing demonstrates that inter-regional disparities have become the dominant source of spatial inequality, with their contribution rising from 60% to 75%. Spatial autocorrelation further exposes a deepening core–periphery polarization and persistent low-performance spatial lock-in. Crucially, the mechanism analysis identifies a pronounced infrastructure-based carbon lock-in. While economic capacity and green patents stimulate GIP, road network density exerts a significant negative effect, reflecting a systemic path dependence on high-carbon road freight. The findings suggest that decarbonizing transport logistics requires shifting from singular technological investments toward multimodal transport restructuring, overcoming physical network dependencies, and promoting the regional diffusion of green innovations. These findings provide an empirical basis for differentiated green-logistics policies, coordinated low-carbon transport infrastructure planning, and cross-regional diffusion of green technologies. Full article

Improving natural resource total factor productivity (NTFP) is sential for easing resource and environmental constraints, advancing high-quality development, and supporting China’s dual-carbon goals. This study aims to evaluate the static efficiency, intertemporal productivity change, regional heterogeneity, and hierarchical frontier differences in environmentally adjusted natural resource total factor productivity in China’s aggregated agriculture, forestry, animal husbandry, and fishery sector from 2008 to 2020. To achieve this aim, we apply a non-radial, non-oriented undesirable-output SBM-DEA model under constant returns to scale, national, regional, and province-specific frontier comparisons. A Global Malmquist–Luenberger index is further used to decompose productivity change into efficiency change and technical change. The analysis shows an overall upward trajectory in national mean NTFP, a persistent eastern advantage, and co-movement among provincial, regional, and national frontier indices, while the dynamic productivity change indicates that productivity improvement was driven mainly by technical change rather than efficiency change. Sensitivity analyses using alternative undesirable-output specifications and cluster-based groupings support the robustness of the main conclusions and inform more targeted discussion and policy implications. Full article

Reducing poverty and income inequality is a major objective of contemporary social policy. Considerable financial resources are spent on social protection to support this purpose. Since these resources are limited, their efficient use is important for improving poverty and income inequality outcomes. The aim of this study is to assess the efficiency and productivity of social protection expenditure in eleven Central and Eastern European (CEE) EU Member States through Data Envelopment Analysis (DEA) and the Malmquist Productivity Index (MPI). Three input-oriented DEA models are estimated for 2016–2023, including two poverty-oriented models and one income-distribution-oriented model. The results indicate high efficiency and scale efficiency across countries, but favourable results in both poverty-oriented models are not necessarily accompanied by similar results in the income-distribution-oriented model. MPI decomposition shows that productivity dynamics in the poverty-oriented models reflect mainly technical efficiency changes before the COVID-19 pandemic and technological change thereafter, whereas productivity dynamics in the income-distribution-oriented model reflect mainly technical efficiency changes throughout the period. Full article

The embedded finance paradigm is fundamentally restructuring digital economies by seamlessly integrating financial services into non-financial digital infrastructures. This study dynamically evaluates the productivity frontiers of Sea Limited’s embedded fintech operations (SeaMoney) across six core geographic markets (Indonesia, Thailand, Vietnam, the Philippines, Malaysia, and Brazil) over the 2023–2026 temporal horizon. Employing a rigorous Panel Data Envelopment Analysis (DEA) Malmquist Productivity Index framework, the research measures systemic performance by analyzing Sales & Marketing (S & M) Expenses and the undesirable Non-Performing Loan (NPL) rate as inputs, against Gross Loan Outstanding as the primary credit output. Before model execution, robust isotonicity was empirically validated using Pearson correlation matrices. The empirical findings reveal profoundly robust systemic performance across the global ecosystem, driven primarily by overarching algorithmic innovations captured by the Technical Change (TC) index. However, this technological boundary exhibits a stabilizing deceleration over time, indicative of a maturing ecosystem transitioning from explosive, frontier-shifting innovation to optimized refinement. Furthermore, localized managerial optimization, measured by the Efficiency Change (EC) index, displays significant regional heterogeneity. While markets like Brazil demonstrated aggressive late-stage efficiency spikes, and core Southeast Asian markets (such as Vietnam and the Philippines) maintained highly stable, competitive trajectories, other regions, such as Thailand, experienced notable managerial regression. This regression signals severe internal frictions in optimizing local marketing budgets against rising credit defaults. Managerial Implications: These findings provide critical strategic insights for orchestrators of the multinational e-commerce ecosystem. The empirical evidence suggests that relying exclusively on centralized technological scaling, such as unified platform infrastructure and global AI architectures, is insufficient for sustained operational growth. To maintain a competitive advantage, operations executives must deploy hyper-localized resource-allocation and customer-acquisition frameworks tailored to specific regional market dynamics and consumer behavior. Sustainable scaling in cross-border digital commerce requires a precise dynamic equilibrium: leveraging robust global technological infrastructure while executing highly adaptive, market-specific operational and marketing optimizations to maximize customer lifetime value (CLV), eliminate customer acquisition waste, and streamline localized transaction and engagement cycles. Full article

Green total factor productivity (GTFP) is an important indicator for assessing urban green development under resource and environmental constraints. Using panel data from 110 prefecture-level cities in the Yangtze River Economic Belt from 2007 to 2023, this study examines changes in urban green total factor productivity. A super-efficiency slack-based measure model that includes undesirable outputs is adopted to measure GTFP, while the Malmquist–Luenberger index is used to decompose its dynamic changes. Spatial variation is then analyzed through trend surface analysis, center-of-gravity migration analysis, spatial pattern analysis, and the geographical detector model. The results indicate that GTFP in the Yangtze River Economic Belt improved on the whole, but its growth did not follow a smooth upward path. Among the decomposed effects, technological progress (TC) was the main source of improvement. Clear spatial differences were also observed. Cities in the middle and lower reaches generally had higher GTFP levels than those in the upper reaches, although this gap became less marked over time. The center of gravity of GTFP stayed mainly in the middle reaches and shifted gradually toward the northeast. The driving factors behind spatial differentiation were not constant. In the early stage, energy intensity and economic development level had stronger effects, whereas technological innovation, human capital, and industrial structure upgrading became more influential in the later stage. These findings provide empirical support for differentiated green development policies and coordinated regional governance in the Yangtze River Economic Belt. Full article

Municipal wastewater systems are increasingly expected to support sustainable urban water management by improving pollutant removal while limiting carbon burdens, yet the plant–network structure behind system performance remains insufficiently understood. This study assessed nine prefecture-level cities in Fujian, China, during 2015–2024 using pollution–carbon accounting with Monte Carlo uncertainty analysis, a linked dual-subsystem network SBM model, a Global Malmquist–Luenberger (GML) index, and second-stage fractional regression. Treated wastewater volume, reclaimed water reuse, and COD removal increased substantially, while net GHG emissions rose from 36.41 × 10⁴ t CO₂-eq in 2015 to 49.84 × 10⁴ t CO₂-eq in 2024. However, net GHG intensity declined per treated wastewater volume and per COD removal, indicating improved carbon intensity despite rising total emissions. Mean overall efficiency was 0.690, with lower network-side than plant-side efficiency (0.558 versus 0.892), identifying collection and conveyance as the main bottlenecks. Dynamic performance was broadly stable (mean GML = 1.001). Fractional regression showed that sewer density was negatively associated with overall and network-side efficiency, especially under higher hydraulic loading. Wastewater-system improvement should therefore shift from capacity expansion alone toward coordinated plant–network optimization to support sustainable wastewater management. Full article

Against the backdrop of the dual-carbon strategy, China’s civil aviation industry, as a high-energy-consumption and high-carbon-emission sector, faces mounting pressure for low-carbon transformation. As the dominant airlines within China’s civil aviation system, Air China, China Eastern Airlines, and China Southern Airlines play a pivotal role in guiding the industry’s high-quality development. Employing the Global Malmquist–Luenberger (GML) index model, this study constructs a global production frontier incorporating undesirable outputs to systematically measure the dynamic evolution of total factor productivity (TFP) for the three major airlines in the period 2005–2023, and further applies a combined static-dynamic regression framework to identify the firm-level heterogeneous mechanisms through which explanatory factors operate. The results reveal significant heterogeneity in TFP trajectories: China Southern Airlines exhibits the most stable efficiency with the lowest volatility; China Eastern Airlines displays the greatest volatility but the strongest post-crisis rebound; and Air China occupies an intermediate position in both efficiency level and volatility. This differentiation stems from fundamental differences in market positioning, strategic orientation, and resource allocation patterns. Market competitiveness exerts a significantly positive effect on TFP for both Air China and China Eastern Airlines. Technological innovation investment generates short-run negative effects across all three airlines, albeit with divergent magnitudes. Human capital accumulation acts as a positive driver for Air China but produces a negative effect for China Southern Airlines, attributable to a structural mismatch between aggressive talent upgrading and organizational absorptive capacity. Shifting the unit of analysis to the firm level, this study identifies three heterogeneous strategic archetypes—market-led, scale-expansion, and regional-deepening—and constructs a differentiated “one firm, one policy” framework to provide targeted policy guidance for improving airline efficiency and facilitating low-carbon transition under carbon constraints. Full article

The measurement and improvement of urban land use efficiency (ULUE) are crucial for sustainable development in China’s Canal Cities (CCCs). Drawing on the theories of production factors, spatial externalities, and agglomeration economy, this study proposes a framework that explicitly addresses the trade-offs and synergies of sustainable land use. A comprehensive ULUE evaluation index system was established. The super-SBM (Slack-Based Measure) and Global Malmquist–Luenberger (GML) index models were employed to assess the green efficiency of urban land use from 2002 to 2023, while Kernel Density Estimation (KDE) and the optimal parameters-based geographical detector (OPGD) model were used to investigate the spatiotemporal evolution and influencing factors of ULUE. The results reveal a distinctive V-shaped trend in efficiency, marked by significant spatial disequilibrium and predominantly technology-driven sustainable growth. Furthermore, ULUE exhibits a spatial distribution characterized by bipolar and multipolar differentiation, accompanied by concurrent concentration and dispersion, with high-value clusters dominating the spatial clustering type. Government regulation emerges as the dominant factor influencing ULUE, underscoring the pivotal role of policy intervention in guiding the sustainable development of land use. The interactions among pairs of influencing factors strengthened over time; notably, the interaction between government regulation and other factors is the strongest. Four-quadrant analysis profoundly reveals the underlying mechanism, distinguishing a high-quality, sustainable development model driven by technological innovation and a resource-dependent economic growth model. The findings provide valuable insights for promoting green development and formulating sustainable land use policies in CCCs. Full article

Sustainable industrial transition requires productivity evidence that separates real efficiency improvement from scale expansion, capital deepening, and reporting change. This study develops a reproducible frontier-productivity diagnostic for provincial leading industry policy, using official 2013–2023 sector panels for 23 two-digit manufacturing sectors in Shandong Province and a matched 2019–2023 benchmark against Jiangsu. The framework combines input-oriented Banker–Charnes–Cooper (BCC) data envelopment analysis (DEA), Simar–Wilson bootstrap bias correction, Malmquist total factor productivity change (TFPCH) decomposition, producer price index (PPI) deflation diagnostics, scale-productivity classification, and targeted sensitivity tests. Bootstrap correction lowers mean BCC efficiency from 0.77 to 0.69 in Shandong and from 0.79 to 0.70 in Jiangsu. Uniform provincial PPI deflation leaves constant-returns-to-scale (CRS) Malmquist estimates almost unchanged, whereas asymmetric deflation creates measurable sensitivity. Direct sector-cluster resampling places Shandong’s aggregate TFPCH at 1.016 with a 95% interval of 0.995–1.045, supporting a near-stationary interpretation rather than a broad upgrading surge; Jiangsu’s corresponding estimate is 0.976 with a 95% interval of 0.955–0.997. The study does not measure environmental performance directly. It shows how frontier-productivity evidence should be stress-tested and paired with environmental indicators before it is used in sustainability-oriented industrial policy. Full article

The sustainability of olive oil cooperatives is a critical issue for the stability and competitiveness of the Mediterranean agri-food sector in an environment of increasing environmental pressures, structural constraints, and changing market conditions. This study assesses the performance and sustainability of 33 Greek olive oil cooperatives over the period 2020–2024 using a non-radial Maximum Distance from the Frontier (MDF) DEA model and the Malmquist Productivity Index. The empirical results highlight strong structural heterogeneity in performance, with average technical efficiency under constant returns to scale remaining relatively low (mean CRS efficiency = 0.49) and scale efficiency averaging 0.77, indicating that a large share of cooperatives operates far from the best-practice frontier and below optimal production scale. The diachronic evolution of productivity appears moderate and unstable, driven mainly by improvements in relative efficiency rather than technological progress. The Malmquist analysis reveals an average technological change index below unity (mean TECHCH = 0.84), reflecting the slow diffusion of modern processing and standardization technologies across the sector. By integrating static and dynamic efficiency analyses, the study provides a comprehensive assessment of productivity dynamics and sustainability challenges in Greek olive oil cooperatives. The findings contribute to a deeper understanding of structural inequalities and highlight the economic foundations of sustainability in cooperative agri-food systems, offering directions for the transition toward more sustainable and competitive production patterns. Full article

Manufacturing productivity in ASEAN has become increasingly important as the region joins global value chains and recovers from COVID-19 disruptions. However, it remains uncertain whether output growth results from true productivity improvements or from changes in factor utilization. This study analyses the efficiency and productivity trends in six ASEAN countries, namely Indonesia, Malaysia, the Philippines, Singapore, Thailand, and Vietnam, from 2000 to 2022. It employs an input-oriented Data Envelopment Analysis (DEA) under both constant and variable returns to scale to assess technical, pure technical, and scale efficiency, while the Malmquist Productivity Index (MPI) decomposes total factor productivity into efficiency and technological change. Results show significant variation among countries and over time. Singapore and Malaysia consistently stay near the regional production frontier, whereas Indonesia, Thailand, and Vietnam lag in efficiency despite strong output and investment growth. Productivity shifts mainly stem from technological advances rather than efficiency gains, especially slowing during the pandemic. Scale inefficiency remains a key performance issue, particularly for Indonesia and Singapore, worsening after 2020. These insights indicate that ASEAN manufacturing competitiveness hinges not only on capital investment but also on converting inputs into productivity improvements. This study offers a comparative analysis of ASEAN manufacturing efficiency and productivity performance. Full article