Ecological efficiency, at its core, is a deceptively simple idea: how much economic and ecological benefit does a city squeeze out of every unit of land, capital, labor, and energy it consumes, while accounting for the pollution it dumps back into the environment? Unlike traditional land-use efficiency measures that reward raw growth, this metric deliberately penalizes cities for industrial wastewater, sulfur dioxide, and soot emissions. The concept, first formalized in 1990, has evolved over three decades into a cornerstone of sustainable development analysis, and China’s researchers have increasingly turned to it as the country grapples with the environmental bill of its unprecedented urbanization drive.

The research team, led by Tonghui Li and colleagues at Capital University of Economics and Business in Beijing, deployed a sophisticated measurement tool known as the super-efficiency EBM model. Unlike conventional data envelopment analysis, this approach incorporates slack variables and undesirable outputs directly into its objective function, allowing researchers to compare cities against a common efficiency frontier across the entire 2010 to 2023 study period. Inputs included built-up land area, fixed asset investment, year-end employment, and an energy consumption index derived from satellite nighttime light data, calibrated across two different satellite systems to ensure consistency. Outputs ranged from GDP and green coverage rates to industrial wastewater and air pollutant emissions.

The headline result is striking: average urban ecological efficiency climbed from 0.541 in 2010 to 0.748 by 2023, with growth accelerating markedly after 2016. Between 2010 and 2014, most cities scored below 0.5 on the efficiency scale, reflecting an era when economic slowdown, accumulated environmental pressures, and rapid urban land expansion constrained pollution abatement. By the 2020 to 2023 window, however, the number of high-efficiency regions had expanded significantly, with clusters emerging in Northeast China, the Yangtze River Delta, and the Yangtze River Basin. The researchers caution that the COVID-19 pandemic may have temporarily suppressed energy consumption and emissions in some regions, and they do not claim to have isolated the causal effect of any single policy.

Yet the national average conceals pockets of deep trouble. The study identifies what it calls efficiency collapse zones in provinces such as Shandong, Henan, and Shanxi, where ecological efficiency actually declined even as the rest of the country improved. These are not random outliers. In Shandong, the depression is concentrated in traditional industrial cities of the west; in Henan, in resource-based cities of the north; and in Shanxi, in heavy chemical industry bases of the south. All three regions share a structural signature: a heavy reliance on coal and energy-intensive industries that have proven difficult to decarbonize quickly.

Perhaps the most technically intriguing finding concerns convergence, the question of whether China’s cities are growing more alike or drifting further apart in ecological performance. The answer is nuanced. The coefficient of variation, a measure of relative dispersion, actually rose from 0.128 in 2010 to 0.214 in 2023, meaning there is no evidence of sigma-convergence, the statistical term for a narrowing gap between cities. But when the team applied a Spatial Durbin Model with two-way fixed effects, controlling for the fact that nearby cities influence one another, they found strong evidence of beta-convergence: cities that started with lower ecological efficiency grew faster than their higher-performing peers, a classic catch-up pattern rooted in neoclassical growth theory.

The convergence numbers carry real weight. Absolute beta-convergence proceeds at a speed of 0.0410 per year, implying a semi-convergence period of roughly 16.9 years, the time it takes for half of the existing gap to close. Conditional beta-convergence, which accounts for factors such as foreign openness, industrial structure, government economic influence, and environmental regulation, is slightly faster at 0.0419, with a semi-convergence period of 16.54 years. The fact that conditional convergence outpaces absolute convergence suggests that these structural factors actively shape how quickly laggard cities close the gap, a finding the authors say fills a gap in prior studies that identified convergence without explaining what drives it.

To dig into those drivers, the researchers ran two complementary analyses. An ordinary least squares regression, using one-period lagged explanatory variables and winsorized data to guard against outliers, revealed that industrial structure, economic development level, and technological level all show positive associations with ecological efficiency, with industrial structure adjustment producing the strongest positive effect. Conversely, foreign openness, local government economic influence, and environmental regulation level showed negative associations, with government economic intervention carrying the largest negative coefficient at -0.247. The authors interpret the environmental regulation result cautiously, noting that end-of-pipe treatment such as sewage processing requires substantial energy and resource inputs in the short term, which may temporarily depress measured efficiency.

But averages can mislead when dealing with a country as vast and varied as China, so the team turned to spatiotemporal geographically weighted regression, or GTWR, a technique that allows the relationship between each factor and ecological efficiency to vary by both location and time. The GTWR model dramatically outperformed the global OLS benchmark, achieving an R-squared of 0.435 versus 0.200, with lower residual error and a far better information criterion score. This confirms that the forces shaping ecological efficiency are not uniform across the country; they shift from city to city and year to year in ways a single national coefficient cannot capture.

The GTWR maps reveal a fascinating geographic evolution. In 2010, the negative association between foreign openness and ecological efficiency was widespread, consistent with the pollution haven hypothesis, in which pollution-intensive foreign investment relocates to less regulated regions. By 2023, positive associations had expanded across western China, suggesting that the composition of foreign investment and local technological conditions had shifted. The beneficial role of a service-oriented industrial structure, once concentrated in the Yangtze River Delta, had spread to provincial capitals and hub cities in the Chengdu-Chongqing and Wuhan metropolitan areas. Technological level’s positive influence also widened its footprint, while the areas where environmental regulation showed negative effects shrank notably, possibly reflecting a transition from narrow pollution-control measures toward more integrated environmental governance.

The authors are careful to frame their findings as statistical associations rather than causal proof, acknowledging that lagged variables cannot fully eliminate reverse causality or omitted-variable bias. Still, the policy implications are hard to ignore. For high-efficiency regions like the Yangtze River Delta, the combination of industrial upgrading and technological development offers a template. For struggling regions, the study points toward reducing dependence on energy-intensive industries, strengthening green technology commercialization, and rethinking fiscal mechanisms that may inadvertently reward resource-intensive growth. As China pursues its dual carbon goals of peaking emissions before 2030 and achieving neutrality before 2060, understanding which cities are converging, which are falling behind, and why could prove decisive for the next phase of the world’s largest urban sustainability experiment.

Subject of Research: Spatiotemporal evolution and convergence of urban ecological efficiency across Chinese cities

Article Title: Analysis of the spatiotemporal evolution, convergence trends, and influencing factors of ecological efficiency in Chinese cities

Article References: Li, T., Gao, W., Li, Q., & Li, X. (2026). Analysis of the spatiotemporal evolution, convergence trends, and influencing factors of ecological efficiency in Chinese cities. Frontiers in Environmental Science, 14, Article 1938089. https://doi.org/10.3389/fenvs.2026.1938089

Image Credits: AI Generated

DOI: 10.3389/fenvs.2026.1938089

Keywords: ecological efficiency, Chinese cities, super-efficiency EBM model, spatial convergence, GTWR, Spatial Durbin Model, urban sustainability, environmental regulation, industrial structure, beta convergence, dual carbon goals, nighttime light data

The conceptual foundation of the work is regional metabolism, an idea borrowed from industrial ecology that frames human settlements and economies in biological terms. Just as an organism consumes nutrients, converts them into useful energy, and excretes waste, a regional economy ingests fossil fuels and electricity, transforms them into economic output, and emits carbon dioxide and other pollutants along the way. In this framing, provinces are nodes, and the trade links between them are the vessels through which embodied energy and carbon travel. The metaphor is more than rhetorical: it allows the researchers to borrow analytical tools from network science and ecology to ask quantitative questions about efficiency and vulnerability that conventional province-by-province accounting cannot answer.

To build the network, the authors turned to environmentally extended multi-regional input-output tables for 2010, 2012, 2015, and 2017, covering China’s provincial economies. These tables record the flows of goods and services between every pair of provinces, and by combining them with provincial energy consumption data and carbon emission inventories, the researchers could trace exactly how much energy, and how much associated carbon, is embedded in each interprovincial transaction. This environmentally extended approach is technically demanding because it distinguishes between emissions produced within a province and those generated elsewhere to satisfy that province’s consumption, exposing the often-hidden transfer of environmental burden from energy-producing interior provinces to coastal manufacturing and consumption centers.

On top of this flow matrix, the study constructs two complementary indicators. The first is network ecological efficiency, a measure of how much economic value each interregional energy pathway delivers per unit of environmental burden, essentially asking which routes through the network convert energy into prosperity with the least carbon cost. The second is network ecological resilience, assessed using social network analysis, a suite of techniques developed in sociology that quantify the structure of relationships among nodes. Measures such as connectivity, centrality, and accessibility reveal how the network is organized, which provinces act as hubs, and how easily the system would absorb the shock of losing a particular link or node.

The results are striking. In 2017, network ecological efficiency varied enormously across China’s interprovincial energy pathways, with the route connecting Sichuan to Jiangsu standing out as the most resource-efficient corridor in the entire network. This path channels relatively clean hydropower-rich Sichuan energy toward the industrial powerhouse of Jiangsu, delivering high economic value with comparatively low emissions. Other corridors performed far worse, moving carbon-heavy energy at a much higher environmental cost per unit of output. Over the study period, the authors found a clear trend toward regional differentiation, in which a subset of provinces pulled ahead in efficiency while others lagged, widening the gap between the metabolic performance of China’s leading and trailing regions.

The structural analysis proved equally revealing. China’s energy network, the study finds, is organized hierarchically, with a small number of highly connected hub provinces commanding disproportionate influence over flows, while many peripheral provinces maintain only thin connections to the core. Adjacent regions tend to be more tightly interconnected with one another, forming clustered neighborhoods of exchange, whereas isolated areas contain few nodes and limited alternative pathways. This topology has a double edge. Dense clustering can foster efficient local collaboration, but it also means that disruptions hitting a hub province or a critical corridor can cascade through dependent regions, while isolated nodes lack the redundant connections that would let them reroute supply in a crisis.

Perhaps the most policy-relevant finding concerns spatial externalities, the spillover effects by which one province’s efficiency or resilience shapes outcomes in its neighbors. The analysis detected significant externalities in both network ecological efficiency and network ecological resilience across China, meaning that no province can fully optimize its energy metabolism unilaterally. A province that improves the carbon intensity of its energy trade benefits not only itself but also the regions linked to it, while a fragile, poorly connected province transmits vulnerability to its partners. This interdependence undermines the traditional logic of provincial-level environmental governance, in which each jurisdiction pursues its own targets, and instead points toward the necessity of coordinated, network-aware policy design.

Building on these findings, the authors propose three priorities for improving China’s energy metabolism. The first is to strengthen cross-regional collaboration, formalizing the mechanisms by which provinces jointly manage shared energy corridors and carbon budgets rather than treating interprovincial flows as externalities. The second is to optimize ecological governance by targeting the specific pathways with the worst efficiency performance, channeling cleaner energy sources and cleaner technologies into the corridors where the marginal environmental gains are largest. The third is to reinforce resilience in vulnerable areas, adding redundancy and connectivity to isolated provinces and reducing the systemic dependence on a handful of hub nodes whose failure would ripple across the network.

The timing of this work is significant. China has pledged to peak its carbon emissions before 2030 and to achieve carbon neutrality by 2060, goals that require not only deploying renewable energy at staggering scale but also reorganizing the geography of energy production and consumption. Studies of this kind illuminate the plumbing beneath the headline targets. They show where embodied carbon actually travels, which corridors waste the most energy per unit of economic output, and which structural weaknesses could sabotage decarbonization efforts when shocks arrive, whether those shocks are political, economic, or climatological. The multi-year input-output approach also demonstrates the value of tracking the system over time, capturing trends such as the growing differentiation in efficiency that a single snapshot would miss.

Methodologically, the study’s marriage of environmentally extended input-output accounting with social network analysis offers a template that researchers can apply well beyond China. Any large economy with strong internal trade linkages, from the United States to the European Union to India, could be modeled as a regional metabolic network, and the paired efficiency-resilience framework could be extended to other critical resource systems such as water, food, and materials. The broader lesson is that sustainability is as much a property of relationships as of places. Provinces, like organs in a body, live or die together, and designing energy systems for the coming decades will require treating the network itself, not the individual region, as the fundamental unit of governance and care.

Subject of Research: Network ecological efficiency and resilience of China's interprovincial energy system analyzed through a regional metabolism framework

Article Title: Ecological efficiency and resilience of energy networks in China: a regional metabolism perspective

Article References: Yan, X., & Wang, F. (2026). Ecological efficiency and resilience of energy networks in China: a regional metabolism perspective. Journal of Industrial Ecology. https://doi.org/10.1007/s44498-026-00174-1

Image Credits: AI Generated

DOI: 10.1007/s44498-026-00174-1

Keywords: China, energy networks, regional metabolism, ecological efficiency, ecological resilience, multi-regional input-output analysis, social network analysis, carbon emissions, interregional energy flows, spatial externalities, industrial ecology, sustainable development