When Firms Go Digital and Green Together, Workers Get More Productive

Every boardroom now has two transformation agendas on the table. One promises that digital technology will revolutionize how the firm processes information, coordinates production, and controls quality. The other insists that green transformation will restructure energy use, production processes, and environmental management. In most companies, these two agendas are planned through separate budgets, separate departments, and separate performance targets. A new study of Chinese listed firms suggests that this separation may be quietly costing firms one of the most valuable prizes of the twin transition: higher output from every worker on the payroll.

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