The term cheap here does not refer to market price. Celi borrows a distinction made famous by the 1998 Scientific American article The End of Cheap Oil, where cheap described the relative ease of accessing and extracting a resource under prevailing technological, energetic, infrastructural and economic conditions. Applied to stone, this leads to the concept of easy marble: the progressive reduction of deposits that can be exploited with limited overburden removal, modest infrastructure and relatively low energy inputs. What gradually declines, in this framework, is not the total amount of marble contained within the massif but the proportion that can be extracted under favorable conditions.

The historical record assembled by Celi is striking. For most of the two-thousand-year history of Apuan quarrying, the basic methods of detaching, shaping and transporting large stone blocks remained fundamentally unchanged from classical antiquity until the nineteenth century, as the archaeologist J. B. Ward-Perkins noted. Quarrying relied on manual labor, wedges, simple iron tools, ropes and gravity-assisted transport, demanding considerable practical skill but comparatively little external energy. Even after the helicoidal wire saw appeared in the nineteenth century, Carrara was still employing methods that differed only marginally from those used in antiquity.

Everything changed after World War II. The introduction of diesel-powered machinery, diamond-wire cutting systems, pneumatic drilling equipment and increasingly mechanized transport, combined with the growing availability of low-cost fossil fuels, profoundly altered the sector’s operating conditions. The numbers tell the story: although the postwar period from 1950 to 2002 represents only about 31 percent of the total 173-year calendar span, it accounts for approximately 69 percent of cumulative production. The preceding 120 years account for the remaining 31 percent. This acceleration coincided with the broader Great Acceleration observed across many environmental and socioeconomic indicators in the second half of the twentieth century.

Labor productivity data reinforce the picture of an energy-intensive industrial transformation. Between 1980 and 1992, productivity across the four principal quarrying districts rose from about 510 to more than 1,080 tons per worker, reflecting the combined effects of technological innovation, greater capital intensity and larger external energy inputs. Notably, this pattern is compatible with a rebound mechanism: improvements in extraction efficiency were accompanied not by a reduction in resource use but by substantially higher extraction rates and a remarkable increase in output per worker. Increasing quantities of external energy progressively replaced human labor, allowing extraction volumes to rise while the workforce declined dramatically.

To test whether the production trajectory follows the dynamics of non-renewable resource exploitation, Celi applied the Hubbert Linearization method, a technique developed for oil depletion analysis, to the cumulative production series. Applied to the complete historical record from 1858 to 2002, the method fails to provide a satisfactory representation, with a low coefficient of determination of 0.168. But over the more recent 1970 to 2002 interval, the linearization becomes substantially more coherent, with R² = 0.883 and an estimated asymptotic cumulative production of 82.112 million tons, within a 95 percent confidence interval of 74.317 to 89.908 million tons. Celi calls this quantity the easy-marble recoverable resource, emphasizing that it represents an estimate associated with the extraction regime prevailing during 1970 to 2002, not an estimate of the total geological resource.

Sensitivity analyses confirm the robustness of this estimate. Testing three interpolation schemes for the pre-1950 production series, linear, PCHIP and modified Akima, while constraining the reconstruction to reproduce an independent estimate of 20 million tons of marble produced between 1830 and 1930, the estimated easy-marble recoverable resource falls within a narrow range of 77.9 to 82.8 million tons. A deliberately conservative cubic spline stress test yields substantially lower estimates of 51.3 to 52.7 million tons with markedly poorer fits, and is retained only as a stress test. The agreement between the constrained reconstructions and the independent postwar Hubbert Linearization provides an additional consistency check.

Energy assessments of modern marble production chains elsewhere in the Mediterranean provide an indication of the magnitude of the energy requirements involved. Cutting and finishing account for more than 80 percent of the energy demand in the production chain, whereas the three quarry extraction operations considered, drilling, diamond-wire sawing and excavator operation, account for only about 8 percent, with transportation accounting for a further roughly 7 percent. As an order-of-magnitude calculation, the 1995 production peak of approximately 1.66 million tons corresponds to roughly 0.615 million cubic meters of marble, which at an indicative energy requirement of 1.7 gigajoules per cubic meter implies an energy demand of about 1.05 petajoules. Celi is careful to note that these calculations do not demonstrate that quarrying energy in the Apuan Alps has increased over time because of declining accessibility; rather, abundant external energy should be understood primarily as an enabling factor that expanded the technical capacity to exploit increasingly complex resources.

The production decline observed after the mid-1990s, when output peaked around 1.66 million tons, cannot be explained by geological factors alone. Economic reports published by the local Chamber of Commerce document a prolonged restructuring of the marble sector, characterized by declining local employment, increasing concentration of quarrying activities, a growing share of raw stone exports and a progressive weakening of local processing industries. During the early 2000s, the sector experienced a marked economic contraction, with reductions in production, turnover, investment and export revenues. Institutional and spatial constraints also shaped accessibility: the Apuan Alps Regional Park was established in 1985 with an area of approximately 54,000 hectares, and in 1997, only two years after the production peak, its perimeter was substantially reduced to 20,598 hectares, with quarrying areas incorporated into the regulatory framework as contiguous quarry areas.

The broader significance of the study extends well beyond the Apuan quarries. The case illustrates how abundant external energy can profoundly modify the relationship between industrial society and geological resources, progressively relaxing the physical constraints that historically limited extraction while continuously redefining the concept of economically recoverable reserves. Within this framework, reaching a production peak does not imply the physical depletion of the resource but signals the beginning of a phase in which increased technical and energy efforts can no longer compensate for the progressive deterioration of extraction conditions. The Apuan marble district appears to have undergone a gradual transition from a predominantly quarry-based industry toward a mining-like production system, with larger excavation fronts, extensive stabilization works, longer access roads and underground operations becoming integral components of extraction. As with many other non-renewable resources, the limiting factor increasingly becomes not the physical availability of the material itself but the growing quantity of energy, capital and technological complexity required to transform that resource into economically recoverable reserves. Celi proposes that this framework may also help interpret the long-term evolution of other mature mineral districts, offering a valuable perspective on how energy, technology and resource quality interact in the history of extractive industries.

Subject of Research: Non-renewable resource economics and energy constraints in marble quarrying

Article Title: The end of cheap Italian marble. Long-term production dynamics and energy constraints in the Apuan marble district (1830–2021)

Article References: The end of cheap Italian marble. Long-term production dynamics and energy constraints in the Apuan marble district (1830–2021). (n.d.). https://doi.org/10.3389/fsrma.2026.1947607

Image Credits: AI Generated

DOI: 10.3389/fsrma.2026.1947607

Keywords: Apuan Alps, Carrara marble, resource depletion, Hubbert peak, energy intensity, quarrying, labor productivity, easy marble, non-renewable resources, Great Acceleration, mineral economics, Italy

The research, published in Frontiers in Environmental Science, was conducted by Juhao Lai, Guangxing Hu, and Bin Li of the School of Economics and Trade at Hunan University in Changsha. Rather than asking whether digitalization or greening alone improves firm performance, the team asked a sharper question: does the coordinated development of a firm’s digital and green subsystems go hand in hand with higher labor productivity? To answer it, they built an unbalanced panel of Chinese A-share listed firms covering 2009 through 2023, a fifteen-year window that spans the rise of China’s digital economy and the deepening of its environmental constraints.

The technical heart of the study is a multidimensional index the authors call digital-green coupling coordination, or DGCC. Drawing on financial statements, annual reports, environmental-governance records, and patent data, they constructed parallel indicator systems for each subsystem following a strategy-investment-technology-application sequence. The digital subsystem combines digital intangible-asset intensity, intelligent-equipment intensity, digital patents identified through official patent-classification mappings, and the frequency of disclosed digital applications. The green subsystem pairs executives’ green awareness with environmental-asset intensity, green patents, and environmental-governance performance. Using entropy-weight TOPSIS, a multi-criteria method that scores each firm-year against a common intertemporal benchmark, they calculated relative-closeness scores for digital capability and green capability, then combined them through a modified coupling-coordination model that captures both subsystem development and relative balance.

The empirical strategy is deliberately conservative. The team estimated firm and year fixed-effects models, which absorb every time-invariant difference between firms, including managerial capability, industry position, and technological endowment, as well as all macroeconomic and institutional shocks common to all firms in a given year. The preferred specification, which adds controls for leverage, cash flow, loss status, sales growth, fixed-asset intensity, and firm age, yields a coefficient of 0.5659 on DGCC, significant at the 1 percent level. Because the estimate is identified from changes within firms over time, the authors translate it into economic magnitude using the within-firm standard deviation of DGCC: a one-standard-deviation increase corresponds to approximately 1.18 percent higher labor productivity. That may sound modest, but it is consistent with the gradual nature of technological upgrading and organizational adjustment rather than an abrupt change in output per worker.

What makes the finding scientifically interesting is what the decomposition reveals. When the authors separated the composite DGCC index into its underlying components, the digital and green subsystem scores were each positively associated with labor productivity, but the mean-centered interaction between them was statistically insignificant. More strikingly, the modified coupling component, which captures relative balance between the two subsystems, showed no independent significant coefficient in the joint specification. The positive relationship was concentrated in the composite-development component, which captures the joint level of both subsystems. In plain terms, a firm can display similar digital and green scores at a very low level without possessing the capabilities needed to reorganize production. Coordination becomes economically relevant only when it is supported by substantive development of both subsystems. Balance alone, the data suggest, is not enough.

The robustness analysis is unusually thorough. The team redefined labor productivity as operating revenue per employee, winsorized the bounded DGCC index, absorbed province-by-year and industry-by-year fixed effects to remove regional shocks and industry-level output-price movements, allowed disturbances to correlate within both firms and years through two-way clustering, and added firm size and capital per worker as additional controls. They also ran first-difference specifications showing the relationship holds in adjacent-year changes, tested temporal ordering by showing current DGCC predicts productivity while future DGCC does not, and examined three-year and five-year long differences confirming the relationship extends beyond short-run co-movement. When they added indicators for low-carbon city pilots, carbon emissions trading, Broadband China, smart-city pilots, and green-finance pilots, the DGCC coefficient remained close to the baseline estimate and statistically significant across all specifications.

Beyond the headline productivity result, the study connects coordinated digital-green development to observable workforce and organizational adjustments. Higher DGCC is associated with a more highly educated workforce: the share of employees holding a bachelor’s degree or above rises with the index. It is also linked to a greater relative allocation of employees to technical functions rather than production positions, measured by the ratio of technical to production employees. And it accompanies a smaller administrative-employment share, consistent with fewer repeated reports, cross-departmental information transfers, and multilayer coordination as digital platforms, environmental-management systems, and production processes become more closely connected. Crucially, the absolute numbers of technical, production, and administrative employees all increase with DGCC, indicating relative task reallocation within an expanding workforce rather than general labor displacement.

The relationship is not uniform across firms. The team constructed three firm-year measures directly connected to labor organization: labor-cost pressure, human-capital level, and routine-task intensity. The DGCC-productivity association is substantially stronger among firms facing lower labor-cost pressure, consistent with those firms having more room to absorb the training and reorganization costs of coordinated transformation. It is also stronger among firms with lower human-capital levels, suggesting greater scope for workforce upgrading rather than an inherent advantage of a less educated workforce. By contrast, the association is stronger in firms with higher routine-task intensity, consistent with greater opportunities to standardize, monitor, and reorganize codifiable tasks. These patterns identify where the twin transition pays off most clearly within the observed sample.

The study also extends the finding to daily operations. Higher DGCC is associated with lower management-expense and period-expense rates and a higher sales cash ratio, meaning revenue is generated with less expense intensity and recognized sales convert more reliably into cash. The log-level estimates qualify this: absolute management and period expenses actually rise with DGCC, but operating revenue grows faster, so the ratios decline. This pattern is consistent with firms supporting a larger scale of activity without a proportionate increase in administrative, selling, and financing costs, and with a tighter connection between operational expansion and cash recovery.

For managers, the message is that digital and green investments should be organized around shared operating problems rather than treated as separate technology and compliance projects. Connecting data on energy use, emissions, equipment operation, production scheduling, orders, and delivery may support maintenance, process adjustment, and resource scheduling in ways that neither agenda achieves alone. The decomposition evidence further warns that nominal balance between weak subsystems is unlikely to be sufficient; coordination matters when both digital and green capabilities have adequate operational depth. For policymakers, the heterogeneity results favor differentiated implementation: firms facing high labor-cost pressure may need more support to absorb reorganization costs, while firms with lower initial human capital or more routine-intensive tasks may have greater scope for upgrading and standardization. The study’s limitations are clear: the sample consists of Chinese listed firms with relatively complete disclosures, and the fixed-effects design identifies within-firm associations rather than plausibly exogenous causal effects. Future research could combine disclosure and patent information with equipment-level, energy-use, emissions, or production-process data to observe the operational links between the two subsystems directly, and could examine whether similar relationships arise in other firm populations, countries, and institutional settings.

Subject of Research: Economics

Article Title: Digital-green coupling coordination and firm labor productivity: evidence from Chinese listed companies

Article References: Lai, J., Hu, G., & Li, B. (2026). Digital-green coupling coordination and firm labor productivity: evidence from Chinese listed companies. Frontiers in Environmental Science, 14, Article 1969383. https://doi.org/10.3389/fenvs.2026.1969383

Image Credits: AI Generated

DOI: 10.3389/fenvs.2026.1969383

Keywords: digital transformation, green transformation, labor productivity, Chinese listed firms, coupling coordination, human capital, Digital-green, coupling, coordination, firm, labor, productivity

The research exploits a natural experiment embedded in Türkiye’s pandemic response. After the country confirmed its first COVID-19 case on 11 March 2020, the government suspended university and secondary education, closed shopping malls, cafes, and entertainment venues, restricted restaurants to takeaway service, imposed travel limits on air and public road transport, and eventually introduced curfews across all provinces. Crucially, these measures targeted specific sectors—transport and storage, accommodation and food services, real estate, and professional, scientific, and technical activities—while leaving manufacturing, mining, construction, trade, information technology, finance, and other industries formally untouched. That sector-specific pattern allowed the authors to designate four restricted sectors as a treatment group and compare their trajectories against a set of unaffected comparison sectors, before and after the restrictions took effect.

The method at the heart of the study is difference-in-differences, a workhorse of empirical economics. The logic is straightforward: if the treated and control sectors were moving along similar paths before the pandemic, then any divergence afterward can plausibly be attributed to the treatment—in this case, the restrictive measures. The authors estimated models with sector fixed effects, which absorb all time-invariant characteristics of each industry, and month fixed effects, which soak up macroeconomic shocks hitting every sector simultaneously. The coefficient of interest, an interaction between sectoral treatment status and the post-March 2020 period, captures the differential change in outcomes experienced by restricted sectors relative to everyone else. Standard errors were clustered at the sector level, and the parallel-trends assumption was tested formally using pre-treatment data.

The data came from two of Türkiye’s official statistical institutions. Monthly job vacancies and job placements were drawn from administrative records of the Turkish Employment Agency, known as ISKUR, covering seventeen sectors from March 2019 to March 2021. Productivity was harder to pin down at the monthly frequency, so the authors constructed a novel proxy: the consumer-price-index-adjusted turnover index published by TURKSTAT, divided by the number of paid employees in each sector. This revenue-based measure, covering ten sectors over thirty-four months from September 2018 to June 2021, captures short-run changes in real revenue per worker rather than fully adjusted labor productivity—a distinction the authors are careful to emphasize, since turnover and value added can diverge, and the measure ignores capital intensity.

The headline result is striking for its restraint. Across the full sample, the difference-in-differences estimates show no statistically significant association between the restrictive measures and job vacancies, job placements, or the revenue-based productivity proxy. In other words, once common pandemic dynamics were accounted for, sectors that were formally restricted did not fare measurably worse than sectors that were not. The descriptive data do reveal two distinct phases: a temporary dip in logged job vacancies during the initial wave, and a more prolonged but ultimately modest association between restrictions and job placements, with the larger magnitude appearing in the second wave beginning in November 2020. But the aggregate regressions point to a consistent conclusion—the immediate labor market contraction looks more like the footprint of the pandemic itself than of the policy curbs.

The picture becomes more textured when the authors split the economy by formality. Using sectoral informality estimates from earlier research, they classified mining, water supply, construction, wholesale and retail trade, accommodation and food services, and administrative services as high-informality sectors. In these subsamples, the point estimates told a story of vulnerability: job vacancies, placements, and productivity all appeared to decline more sharply in restricted informal sectors, while formal sectors showed relative resilience, with even a marginally positive association between restrictions and job vacancies in the preferred specification. The interpretation is intuitive. Informal firms rely on cash flows, face-to-face interaction, and short-term labor arrangements, leaving little room for remote work or organizational restructuring. Formal firms, by contrast, could pivot to digital tools and telework.

Yet the authors subject their own findings to a demanding robustness check, and the results temper the narrative. With so few sector clusters—as few as four in some specifications, and only a single treated sector in the informal subsamples—conventional cluster-robust standard errors can understate uncertainty. Applying a wild-cluster bootstrap procedure, the study finds that every informal-sector estimate that appeared significant under conventional inference loses statistical significance, with bootstrap p-values ranging from 0.17 to 0.20. The formal-sector job vacancy estimate weakens from conventional significance to a marginal bootstrap p-value of 0.073. Parallel-trends diagnostics also fail for informal-sector job vacancies under both the seasonally adjusted and unadjusted specifications. The authors are transparent about this: the informal-sector heterogeneity is a descriptive pattern warranting further investigation, not definitive statistical evidence.

The methodological care extends to the vacancy data themselves. The authors discovered that seasonal adjustment of short administrative series during an unprecedented structural break can distort inference, potentially misinterpreting shock-driven variation as seasonal movement. They therefore report two specifications for job vacancies—one seasonally adjusted, one raw—and place greater weight on the unadjusted model, which lets the sector and time fixed effects absorb temporal variation without the risk of adjustment artifacts. This kind of methodological self-scrutiny is rare and valuable, particularly in a literature where pandemic-era data are short, noisy, and prone to structural breaks that standard statistical machinery was never designed to handle.

The findings align with a growing international consensus. Research on South Korea, where lockdowns were minimal, documented job destruction comparable to that in the United States and the United Kingdom, suggesting that fear of contagion and voluntary distancing drive much of the economic damage. Real-time tracking studies in the United States similarly found that spending and employment collapsed before or regardless of formal orders. Türkiye’s sector-level evidence adds an emerging-market dimension to this picture, indicating that even in an economy with substantial informality, the aggregate labor market response was shaped more by the pandemic shock than by the regulatory response to it.

The policy implications are sobering. If restrictions themselves were not the primary driver of labor market damage, then lifting them quickly is not a reliable route to economic recovery—and imposing them need not be economically catastrophic if paired with the right support. But the suggestive evidence of informal-sector fragility points to where policy should focus: targeted income support for informal workers and micro-enterprises during closures, and investments in digital and operational resilience that would help the most exposed firms weather future shocks. As the authors conclude, uniform containment measures can generate uneven consequences across sectors with different levels of formality, and economies like Türkiye—where informality and structural dualism remain prevalent—need crisis-response tools calibrated to that heterogeneity. In a world preparing for the next pandemic, that may be the study’s most enduring lesson.

Subject of Research: The impact of COVID-19 restrictive measures on the labor market and productivity in Türkiye

Article Title: Impact of COVID-19 restrictive measures on labor market and productivity in Türkiye

Article References: Dolu, A., & İkizler, H. (2026). Impact of COVID-19 restrictive measures on labor market and productivity in Türkiye. Heliyon, 12(15), Article e45535. https://doi.org/10.1016/j.heliyon.2026.e45535

Image Credits: AI Generated

DOI: 10.1016/j.heliyon.2026.e45535

Keywords: COVID-19, labor market, Türkiye, difference-in-differences, job vacancies, job placements, labor productivity, informal economy, lockdowns, emerging markets, econometrics, public policy