A research team led by Huijun Wu and Chang Su constructed what they call the IEC nexus, a unified framework linking industrial structure, ecological environment and carbon emissions into a single analytical system. The approach borrows from the ‘nexus’ thinking first proposed at the 2011 Bonn Conference for water, energy and food, in which interdependent systems mutually reinforce or constrain one another. Rather than treating economic transformation, pollution and greenhouse gases as separate policy files, the team measured all three simultaneously for Huainan between 2000 and 2022, using entropy-weighted indicators drawn from national and provincial statistical yearbooks, energy inventories and local environmental bulletins. Carbon emissions were calculated following IPCC guidelines, converting the physical consumption of coal, oil and natural gas into carbon dioxide using fuel-specific heating values, carbon contents and oxidation rates.

The centerpiece of the analysis is the coupling coordination degree, a metric that ranges from zero to one and captures how well the three subsystems evolve together rather than at each other’s expense. The results tell a story in three acts. In the early period from 2000 to 2008, the industrial subsystem lagged badly behind, with its evaluation index fluctuating between just 0.13 and 0.44 while environmental and carbon indices ran higher. During a difficult transition from 2008 to 2016, the accumulated costs of coal dependence became visible: the coordination degree actually slipped from 0.753 in 2012 to 0.741 in 2016 as environmental quality briefly deteriorated. Then, from 2016 onward, the system entered what the authors describe as a high-quality development stage, with the overall coordination degree climbing from 0.552 in 2000 to 0.832 in 2022, crossing from ‘barely coordinated’ into the ‘good coordinated development’ band.

Perhaps the most striking finding is how lopsided the progress has been. The industrial structure index surged to about 0.52 by 2020 and the ecological environment index climbed to roughly 0.94, yet the carbon emissions index crawled along at only about 0.40. In other words, Huainan has learned to restructure its economy and clean its air far faster than it has learned to decarbonize. The coupling degree itself remained stubbornly high throughout, ranging from 0.754 to 0.991, which the researchers interpret as evidence that the three subsystems are locked into tight mutual dependence: improvements in one cannot be sustained for long without corresponding movement in the others.

To identify what was actually holding the city back, the team applied an obstacle degree model that decomposes the coordination deficit into individual indicators. The verdict shifted dramatically over the two decades studied. In 2000, the industrial structure subsystem accounted for a dominant 71.04 percent of the total obstacle, driven largely by weak tertiary-sector employment and stagnant rural incomes. By 2022 that share had fallen to 45.07 percent, while the ecological environment subsystem more than doubled its contribution from 15.03 percent to 37.48 percent, and carbon emissions rose from 13.93 percent to 17.46 percent. The bottleneck has migrated: as structural constraints eased, pollution intensity, particularly sulfur dioxide emissions per unit of GDP, and the lingering weight of coal-linked industry became the new front line.

The study then turned from diagnosis to prophecy, pairing the STIRPAT model, a regression framework relating environmental pressure to population, affluence and technology, with a Grey GM(1,1) forecasting model. Because the six explanatory variables, including population, per capita GDP, urbanization, secondary-industry share, energy intensity and energy structure, showed substantial multicollinearity, the team calibrated the model with ridge regression, setting the ridge parameter at 0.14 and achieving an R-squared of 0.868 with an average fitting error of 6.18 percent. The authors are careful to note that only the population coefficient reached even marginal statistical significance, so the projections should be read as conditional scenarios rather than firm causal predictions.

Those scenarios, however, are revealing. Under a business-as-usual pathway, Huainan’s carbon emissions would not peak until around 2035, five years past the national target, at roughly 29.6 million tons, about 1.27 times the 2020 level. Merely optimizing industrial structure barely improves that timing. The low-carbon and energy-saving scenario, which combines faster cuts in energy intensity with a cleaner energy mix, brings the peak forward to 2030 at about 28.7 million tons while preserving steady economic growth, making it the pathway the authors judge most realistic and aligned with China’s dual carbon goals. An aggressive green development scenario peaks earliest, in 2028 at just 24.9 million tons, but only by deliberately throttling population growth and urbanization. At the other extreme, an extensive development scenario that prioritizes raw economic expansion delays the peak to 2040 at a hefty 35.1 million tons.

The Grey model projections for the coordination degree itself add a further layer of nuance. Across all scenarios the index continues rising through 2036, but the green development pathway approaches perfect coordination by 2032, followed by industrial structure optimization, then the low-carbon energy pathway, with the extensive scenario trailing the field. The ordering suggests that structural adjustment delivers the quickest coordination gains, while energy-focused measures, though slower to register, are what actually bend the emissions curve. For a city where coal mining has caused land subsidence, water pollution and ecosystem degradation across some of eastern China’s most representative subsidence zones, both fronts matter.

The policy implications the authors draw are refreshingly specific. Huainan should not simply shrink its secondary sector, they argue, but upgrade its internal composition, cultivating new energy equipment manufacturing, photovoltaic and wind industries, digital economy sectors and modern logistics, while converting mining subsidence zones into wetland parks and ecological conservation areas. Coal-power plants should pursue ultra-low emission retrofits and carbon capture technologies, and the city should build a dynamic monitoring system around the very indicators the obstacle analysis flagged: tertiary employment, pollution intensity, energy intensity and energy structure. Because the constraints themselves shift over time, the authors contend, governance must be adaptive and coordinated across industrial, environmental, energy and planning departments rather than siloed.

The study is candid about its limits. A 2016 administrative boundary change, when Shou County was incorporated into Huainan, complicates comparisons of population-related indicators across the time series, and some missing data were filled by regression-based imputation. The findings from a single coal city may not transfer cleanly to regions with different resource endowments, and the forecasting models rest on assumptions about parameter stability that real-world shocks could upend. The authors also stress that their correlations describe temporal associations, not proven causal effects. Even so, the broader message travels well beyond Anhui: for the hundreds of resource-dependent cities worldwide staring down decarbonization deadlines, Huainan’s two-decade ledger suggests that industrial restructuring, ecological restoration and carbon mitigation can indeed rise together, but only if energy intensity and energy structure are treated as the decisive battleground rather than an afterthought.

Subject of Research: Coupling coordination of industrial structure, ecological environment and carbon emissions in the coal-resource city of Huainan, China

Article Title: Can coal-resource cities achieve high-quality coordination of industry, environment, and carbon? Evidence from Huainan, China

Article References: Wu, H., Su, C., Zhang, Y., Li, M., Kong, X., Liu, Y., & Gao, L. (2026). Can coal-resource cities achieve high-quality coordination of industry, environment, and carbon? Evidence from Huainan, China. Environmental and Sustainability Indicators, 32, Article 101541. https://doi.org/10.1016/j.indic.2026.101541

Image Credits: AI Generated

DOI: 10.1016/j.indic.2026.101541

Keywords: Huainan, coal-resource cities, carbon emissions, coupling coordination degree, STIRPAT model, energy transition, industrial structure, ecological environment, carbon peak, obstacle degree analysis, China dual carbon goals, GM(1,1) forecasting

The research team, led by Xu Yuhui with Batunacun and colleagues, focused on Jiziwan, a region spanning roughly 790,000 square kilometres across Inner Mongolia, Gansu, Ningxia, Shaanxi and Shanxi. Home to about 48 million permanent residents, the area drew approximately 627.8 million tourists and 53.4 billion yuan in tourism revenue in 2023 alone. Yet it is also one of the most ecologically fragile corners of the Yellow River Basin, beset by soil erosion, land desertification and acute water scarcity. That combination of rich natural and cultural attractions and a delicate ecosystem makes Jiziwan an ideal natural laboratory for asking a question that has long dogged tourism scholarship: when a destination’s tourism economy bounces back from shocks, is that resilience anchored primarily in the environment, or in a broader constellation of economic, social and technological forces?

To answer it, the researchers first had to measure resilience itself. They built a comprehensive evaluation system grounded in Martin’s economic resilience theory, capturing four dimensions: resistance ability, the capacity to absorb shocks; recovery ability, the capacity to return to a dynamic equilibrium; reconstruction ability, the capacity to reorganise internal structures under stress; and update ability, the capacity to break path dependence through knowledge and innovation. Twenty indicators fed into this framework, ranging from the density of A-level tourist attractions and tourist arrivals to the number of days of good air quality, green coverage rates, highway passenger volumes, university enrolment and tourism research and development funding. Rather than weighting these indicators subjectively, the team applied a genetic algorithm-optimised projection pursuit model, a technique that projects high-dimensional data into a lower-dimensional space and identifies optimal weightings while sidestepping multicollinearity. An entropy weight method used as a cross-check produced broadly consistent rankings, strengthening confidence in the results.

The measurement revealed a tourism economy on the rise, but an uneven one. The composite resilience index for Jiziwan climbed from 0.175 in 2010 to 0.298 in 2023, dipping in 2020 under the impact of COVID-19 before rebounding to its peak. Kernel density estimation showed the entire distribution shifting rightward over time, but also exposed a persistent double peak: a large peak of cities stuck at low resilience and a smaller peak of high performers, with a right trailing tail signalling entrenched regional disparity. Spatially, resilience declined steadily from the centre of the region to its periphery in every year examined. Cities such as Hohhot, Ordos, Yulin and Taiyuan formed a resilient core, with Taiyuan standing alone at the high-resilience level by 2023, while five western cities, including Alxa, Bayannur, Wuhai, Zhongwei and Baiyin, remained trapped at low levels throughout the study period. Global Moran’s I statistics confirmed weak positive spatial autocorrelation, significant in 2015 and 2020 but not in 2010 or 2023, pointing to localised clustering rather than region-wide spatial dependence.

The methodological heart of the study, and its most novel contribution, lies in how the drivers were identified. Conventional tools each carry well-known limitations: geographic detectors struggle to rank drivers against one another, regression analysis leans on linear assumptions, structural equation models typically yield a single global optimum, and qualitative approaches are subjective and hard to visualise. The team instead turned to partial order theory and the Hasse diagram technique, mathematical tools previously applied to chemical risk assessment, groundwater quality and land degradation, but never before to tourism. Partial order theory ranks comparable elements across multiple criteria without requiring complete ordering, ignores multicollinearity within driver groups, and preserves the information of every indicator. The Hasse diagram technique then renders those partial orders as directed graphs in which maximal elements mark the most strongly influenced cities and chains trace the descending ranking of influence. Fifteen indicators across four driver groups, regional economy, social environment, ecological environment and technological innovation, were compared across 21 cities in three periods: 2010 to 2015, 2015 to 2020 and 2020 to 2023, generating twelve partially ordered sets and twelve Hasse diagrams.

The verdict was unambiguous. In the first period, the ecological environment dominated in 15 of the 21 cities, ahead of social environment with 11, regional economy with 9 and technological innovation with 8. In the second period, the ecological environment again led with 15 cities. By the third period, its reach had expanded to 18 cities, while technological innovation surged from 5 cities in the first period to 11, overtaking the regional economy to claim second place. The regional economy and social environment remained relatively stable influences, with the economy weakening in the northwest and strengthening in the centre-south, and the social environment showing the reverse pattern. The overall picture, the authors conclude, is a multi-dimensional synergistic driving pattern operating under the relative dominance of the ecological environment, a finding confirmed as statistically significant by fixed-effects panel regression in which all four driver groups and nearly all individual indicators showed significant associations with resilience.

Why does the environment loom so large here? The explanation lies in what Jiziwan’s tourism actually sells. Unlike heavily urbanised destinations that trade on cultural landscapes, service infrastructure and urban consumption, this region’s products rest on pleasant climates, vast grasslands, clean rivers, deserts, waterfalls and biodiversity. The quality of the ecological landscape is not one input among many; it is the raw material of the entire tourism economy. Technological innovation, the study argues, functions mainly as a tool for amplifying the value of those ecological resources and improving management efficiency, and cannot substitute for the environmental foundation that shapes attractiveness and resource supply. At the same time, tourism offers ecologically fragile regions a pathway to development that traditional resource-intensive industries cannot, converting ecological advantages into economic benefits through ecotourism, rural tourism and Yellow River cultural tourism, while channelling revenue back into biodiversity protection and environmental education.

Policy has amplified this natural advantage. The researchers identified twelve major ecological policies shaping the region, spanning three tiers: overarching national strategies such as the Three-North shelterbelt project and desertification control; Yellow River Basin-specific programmes linking ecological protection with cultural inheritance and tourism; and local initiatives including wetland restoration, ecological corridors and the development of the Yellow River cultural tourism belt. This cascade of macro-strategic guidance, river-basin support and local implementation has systematically improved environmental quality, raised tourism attractiveness and reinforced the ecological environment’s dominant position in the resilience system. The study also flags the exceptions that prove the rule: in Yulin, Yan’an and Shuozhou, cities at the intersection of the Loess Plateau and the Mu Us Sandy Land whose economies centre on coal and petroleum extraction, mining has so degraded sewage treatment capacity, surface stability and vegetation that the ecological environment exerted no positive influence on resilience in the most recent period.

The practical payoff is a differentiated policy map. The team classified the 21 cities into five strategic categories: ecological restoration-oriented cities such as Alxa, Bayannur, Ulanqab and Linfen, which should prioritise conservation and control development intensity; technology empowerment-oriented cities such as Hohhot, Taiyuan, Yan’an and Luliang, encouraged to deploy virtual and augmented reality and strengthen real-time risk monitoring; economic structure optimisation-oriented cities such as Baiyin, Luliang and Baotou, which can deepen industrial chains and cultivate new tourism business models; infrastructure improvement-oriented cities such as Ordos, Yulin, Baiyin and Wuhai, which need stronger transport, accommodation and public health systems; and regional coordination-oriented cities such as Hohhot, Baotou and Taiyuan, which should build cross-regional governance mechanisms, linked tourism routes and shared markets. Former mining cities, the authors suggest, could pursue green transformation through industrial heritage tourism.

The authors are candid about the limits of their analysis. Because the study area contains only 21 prefecture-level cities, spatial econometric models were not applied, and the results should be read as direct local associations rather than a full account of spatial spillovers between neighbouring cities. Future work with larger samples, longer panels and spatial lag, error or Durbin models could sharpen the picture. Even so, the study delivers a rare quantitative answer to a question usually left to qualitative argument, and its message resonates far beyond the Yellow River bend: in ecologically fragile destinations, protecting the environment is not a constraint on tourism resilience but its very foundation, and the first application of partial order theory to tourism research offers a transferable template for proving it.

Subject of Research: Drivers of tourism economic resilience in the ecologically fragile Jiziwan region of China's Yellow River Basin

Article Title: Ecological environment dominant or multi-dimensional drivers? A study on the drivers of tourism economic resilience in Jiziwan, China

Article References: Yuhui, X., Batunacun, Changan, Kaixin, L., Yufeng, Yongmei, & Dandan, Z. (2026). Ecological environment dominant or multi-dimensional drivers? A study on the drivers of tourism economic resilience in Jiziwan, China. Environmental and Sustainability Indicators, 32, Article 101505. https://doi.org/10.1016/j.indic.2026.101505

Image Credits: AI Generated

DOI: 10.1016/j.indic.2026.101505

Keywords: tourism economic resilience, ecological environment, Jiziwan, Yellow River Basin, partial order theory, Hasse diagram, GA-PP model, ecologically fragile regions, technological innovation, spatial heterogeneity, ecotourism, China