Absolute Environmental Sustainability Assessment (AESA) aims to determine whether a system operates within its allocated share of Carrying Capacities (CCs). Initial guidelines have been proposed to establish a methodological framework. This paper proposes methodological recommendations to address three key requirements for a consistent implementation of AESA, i.e., i) additivity in the CCs downscaling, ii) no double-counting issues in environmental assessment, and iii) consistency of system boundaries between environmental assessment and downscaling steps. Proposals are based on the concept of environmental responsibility. Three environmental accounting approaches, which consider different types of actors responsible for a system's environmental impacts, are employed. These are the production-based approach (PBA), the consumption-based approach (CBA) and the income-based approach (IBA). Furthermore, identifying the responsible actor makes the CCs allocation step more transparent. The adapted AESA method is implemented across country-level economic sectors using Exiobase, an input-output database. Results obtained using the three accounting approaches and two theories of distributive justice are then compared. Similar to the choice of theory of distributive justice, AESA results are sensitive to the chosen accounting approach. Both are normative choices. Based on these preliminary findings, recommendations and further research perspectives are proposed for the AESA community. Finally, a focus on a single sector makes it possible to explore in further detail the relevance of the proposed recommendations for decision-making.
Rapid industrial automation is becoming an important feature of production systems operating under economic and environmental constraints, while its observed performance implications may emerge with delay and differ across regions. This study examines whether industrial robotization and provincial green synergy performance evolve in tandem, where coordination disparities originate, and how their dynamic relationship unfolds. Using panel data for 30 Chinese provincial-level regions from 2006 to 2023, we employ a shift-share measure of robot installation density and estimate green synergy performance with a super-efficiency slacks-based measure integrating economic output, urban greening, industrial pollution, and carbon emissions. The coupling coordination degree evaluates the alignment of the two systems, the Dagum Gini decomposition identifies the sources of regional disparities, and a panel vector autoregression examines reciprocal temporal predictability and lagged responses. The national coordination degree increased from 0.4810 to 0.7740, while the overall Gini coefficient declined from 0.1685 to 0.1009. Between-region differences remained the largest component of disparity, accounting for 51.14% on average, with the eastern-western gap being the widest. Robotization predicted green synergy performance nationally and in eastern China, with a negative first-lag association followed by a positive second-lag association. By period 10, innovations in robotization accounted for 18.63% of the forecast-error variance in green synergy performance nationally and 34.02% in eastern China. These results reveal a stage-dependent and regionally uneven predictive relationship, although the research design does not identify structural causal effects. The findings highlight the value of coordinating robot deployment with green process redesign, region-specific technical support, and full-cycle performance monitoring.
