The widespread use of plastics has resulted in significant environmental challenges, including pollution, landfill accumulation, and harm to ecosystems and human health. As concerns over plastic waste intensify, biodegradable plastics have emerged as promising alternatives that can decompose under specific conditions and contribute to a circular economy. This review examines how biodegradable plastics can help address these issues, beginning with the distinction between biodegradable polymers, which are long-chain molecules, and biodegradable plastics, which are end-use materials created by blending these polymers with additives and fillers. It explores common biodegradable polymers, their origins, production processes, and key physical and chemical properties. Further, the review covers both the compounding stage, in which polymers and additives are combined, and the subsequent product development and processing steps involved in manufacturing of biodegradable plastics. A criterion is proposed to assess and rank biodegradable plastics based on their biodegradability. The review also discusses applications and the sustainability of their value chains. Key challenges to widespread adoption, such as technological limitations, economic concerns, and environmental or health risks, are highlighted. Finally, the review stresses the importance of advancing biomass cultivation, polymer development, processing techniques, and degradation methods to unlock the full potential of biodegradable plastics. Overall, it emphasizes the need for continued innovation to promote sustainable materials and improve plastic waste management.

Social impacts in packaging supply chains remain underexplored compared to environmental and economic aspects, despite their growing relevance in sustainability decision-making. This study applies Social Life Cycle Assessment (S-LCA) to evaluate the social sustainability of three carrier bag types: low-density polyethylene (LDPE) plastic, polylactic acid (PLA) bioplastic and kraft paper. The assessment is conducted using OpenLCA by integrating primary data with the SOCA V2 database, and focuses on four stakeholder groups, including Local Communities, Society, Value Chain Actors, and Workers. Social impacts are quantified across life cycle stages and stakeholder categories. Results are expressed as “medium risk hours,” a relative indicator used for comparative hotspot identification. The results indicate that Society and Value Chain Actors face high to very high risks (>40%) across all materials, highlighting potential areas for improvements. The PLA bioplastic bag exhibits the highest social impacts during raw material production, while the kraft paper bag performs worst in manufacturing and end-of life treatment. Overall social impacts rank PLA bioplastic highest (95.79 medium risk hours), followed by kraft paper (54.45), and LDPE plastic (35.15), a ranking that challenges common assumptions about the sustainability of bioplastics and paper alternatives. This quantitative social impact analysis provides clear, data-driven insights into social risk hotspots and improvement opportunities.

Widespread digital transformation and the rapid expansion of data centres for artificial intelligence (AI) pose growing challenges for electricity systems and global climate targets. Many studies project near-term data centre energy demand, but few extend beyond five years due to the high degree of uncertainty in technological trends and a lack of robust and widely accepted methodologies. This is a problem for climate mitigation research that covers plausible scenario envelopes over longer time horizons to assess carbon budgets and warming outcomes. In this study, we extend data centre energy demand projections to 2050 within the Shared Socioeconomic Pathways (SSP) framework, aligning with common approaches in climate scenario analysis. Our method draws on three main historical periods since 2010 marked by distinctive trends in data centre service demand growth or efficiency gains, combined with projected future trends in digital transformation levels. Across the five SSP scenarios, we find global data centre electricity demand ranges widely, reaching between 1,800 and 5,000 TWh by 2050. We also find that the average weighted carbon intensity of electricity consumed by data centres must fall to below 100 gCO2/kWh from 2030 to remain compatible with 1.5 degrees C climate targets. Unlike most SSP-based studies, we find that uncertainties in data centre energy demand growth as a result of within-sector development and efficiency dynamics are wider than uncertainties from the different socioeconomic development trajectories captured by variation across SSP storylines. Rather than subsuming data centres within the commercial buildings sector as currently, we argue that data centres should be differentiated as a separate end-use sector in global energy statistics and modelling to account for its unique growth characteristics and uncertainties. Our findings provide a structured, scenario-based extension of anticipated near-term data centre energy needs. Our characterisation of uncertainties informs strategies for managing long-term digital infrastructure growth in line with global climate goals.

Greenhouse gas (GHG) rebound and its lagging effects, resulting from disruptive events such as the COVID-19 pandemic, are often underestimated and sensitive to socioeconomic fluctuations. Artificial intelligence models such as artificial neural networks are widely adopted in research studies for GHG forecasting. In this study, a Bayesian-optimized artificial neural network model, complemented with Shapley Additive exPlanations (SHAP), is applied to forecast sectoral GHG emissions and rebound effects under disruptive events based on highly correlated socioeconomic indicators, marking a first-of-its-kind approach. Malaysia is chosen as the case study. Nine sectoral emissions are forecasted under pandemic and non-pandemic circumstances. The forecasted overall GHG emissions indicate that a GHG rebound in Malaysia is apparent by 2022, with 9.56% higher GHG emissions in the pandemic scenario compared to the non-pandemic scenario. The “Industrial processes and product use” sector exhibits a high rebound, surpassing pre-pandemic levels, while the “Transportation” sector experiences a moderate rebound, both contributing to an increase in overall GHG emissions until 2030. SHAP analysis reveals that socioeconomic indicators have varying influences over sectoral emissions, based on ranking and SHAP value magnitude differences. The findings underscore the need for policymakers to reassess their climate goals, considering the repercussions of disruptive events like COVID-19, while also narrowing the emission gap between climate goals and post-pandemic emissions. Sectoral policies are tailored based on the obtained results from the socioeconomic-explanatory ANN model and are ranked following the GHG emissions proportion contributing to the rebound and its lagging effect.

Plastic packaging offers significant potential for improvements compared to other hard-to-abate plastic uses. This study explores greenhouse gas (GHG) emissions and energy consumption of various packaging materials along their value chains using a system dynamic modelling, applied to the EU-27. The replacement potential for low-density polyethylene (LDPE) carrier bags is assessed with alternatives: polylactic acid (PLA) plastics, kraft paper, and natural cotton. Both plastics are considered as single-use products, and kraft paper and natural cotton as multi-use products with low and high reuse levels assumed. Eight scenarios are examined, including four single-product scenarios (plastic as baseline, PLA bioplastic, paper and cotton) and four combined-product scenarios accounting for more complex interactions among material combinations, such as durability, reusability, productivity, and recoverability. In consumer preference scenario (50% cotton, 20% plastic, 20% paper and 10% bioplastic), with low reuse level, GHG emissions increase by 37.7%, while energy consumption decrease by 18.3% compared to the baseline. In contrast, high reuse frequency results in 20.3% decrease in GHG emissions and 45.1% reduction in energy consumption. This study offers valuable contributions to addressing critical environmental challenges (GHG emissions, energy consumption, plastic pollution) and identifying more sustainable systems regarding packaging life cycles.

Single-study regression models for compost total nitrogen (TNF) are calibrated under narrow experimental conditions and show limited transferability to other composting systems. This study develops an interpretable multiple linear regression (MLR) framework to estimate TNF in in-vessel food waste composting. The framework is based on 42 independent composting studies spanning a wide range of scales and feedstock compositions. Missing data were addressed through systematic screening and k-nearest-neighbour imputation. Model performance was evaluated using an independent 80/20 train-test split. The best-performing model (CBase) achieved a test R² of 0.26, RMSE of 0.57, and MAPE of 21.9%. The final equation retained initial TN, aeration rate, grinding treatment, and pile volume as predictors. Monte Carlo resampling across 200 iterations confirmed stable absolute prediction error, with median RMSE of 0.66 and median MAPE of 29%. R² varied substantially across splits, which is expected for small and heterogeneous cross-study datasets where error-based metrics are more reliable than variance-based measures. Benchmarking against published single-system TN equations revealed near-complete failure when applied to the multi-study dataset. Test R² values dropped as low as 4459 and MAPE exceeded 2000%, confirming the severe transferability limitations of narrowly calibrated composting models. The transparent regression equation supports early-stage planning, scenario comparison, and better nitrogen retention management across diverse composting systems. These outcomes support solid waste valorisation, resource efficiency, and circular economy principles in food waste management, contributing to Sustainable Development Goals 12 (Responsible Consumption and Production) and 13 (Climate Action) through reduced waste generation and lower nitrogen-related emissions.

Transboundary water pollution poses persistent challenges for river basin sustainability due to spatial externalities and mismatched environmental responsibilities between upstream and downstream regions. This study develops an allocation and trading framework for wastewater emission permits (WEPs) in the upstream watershed by integrating a cross-provincial watershed eco-compensation scheme (CpWES). A bi-level robust optimization model with uncertain parameters is established, in which the Bureau of Ecology and Environment as the upper-level seeks to maximize eco-economic benefits under uncertainty, while lower-level governments allocate WEPs across sectors to optimize local environmental-economic benefits. The model is applied to the Xin’an River Basin, and the results show that: (1) The eco-compensation policy exhibits an inverted U-shaped impact on the upstream economic benefits, suggesting the need for moderately calibrated compensation levels; (2) Huizhou County exhibits the highest emission reduction potential, with an annual load reduction of up to 394 tons; and (3) Under hydrological uncertainty, urban and industrial sectors achieve 4.5%-11.7% and 10%-16% annual reductions in pollutant loads, respectively, contributing to improved compliance with basin water quality targets. Overall, breaking administrative barriers and establishing basin-wide emission trading mechanisms can markedly enhance the sustainable development in upstream regions of transboundary river basins, providing practical support for upstream water pollution management under cross-provincial eco-compensation policies.

Building Information Modelling (BIM) enables time, cost, and materials savings in building design and construction. However, the promise of BIM is yet to be realised. We assessed the current state-of-the-art in BIM adoption and use, identifying both barriers and opportunities across six dimensions defined by the PESTLE framework (political, economic, social, technical, legal, environmental). We combined market survey, literature review, and new insights from 41 expert interviews with architects, consultants and constructors across 11 European countries. We find BIM is used principally by larger firms as a design and data-processing tool to enable collaboration between project partners. BIM's value proposition is primarily to streamline construction processes not improve resource efficiencies. Barriers to BIM adoption include interoperability issues, split incentives and value chain fragmentation, and weak economic incentives particularly for small firms. In the medium-term we find two important drivers of more widespread BIM adoption. First, institutional investors in the commercial buildings sector are increasingly pushing green certification standards for which compliance is demonstrated by BIM. Second, whole building lifecycle emission regulations for buildings mandated by the EU from 2030 will require BIM calculations. Four pioneer Northern European countries already have similar emission limits in place. Under realistic assumptions, we estimate material savings enabled by BIM in new building construction could deliver 21-31% embodied emission reductions after 10 years.

This study examines the interplay between large-scale rail infrastructure expansion and long-term vegetation dynamics in the Seoul Metropolitan Area, highlighting how integrated urban planning can mitigate or even reverse environmental degradation typically associated with rapid urbanization. Uniquely, Korea's new towns have been systematically developed as transit-oriented districts to alleviate the hyper-concentration of population in Seoul, resulting in a context where railway construction and urban growth are tightly coupled. Contrary to the prevalent narrative of inevitable green space loss, the spatial analysis reveals that several newly developed districts, designed with explicit ecological considerations, demonstrate stable or even increasing vegetation indices over the study period. By employing a combined Geographically and Temporally Weighted Regression framework, we identify localized trajectories where synchronized transit investments and proactive green urbanism policies have yielded net ecological gains. These findings position Korea's policy-driven urban model as an instructive case study for reconciling infrastructure development with ecological resilience, underscoring the critical importance of integrating environmental objectives into transportation and land-use planning.

Digitalisation is a double-edged sword for energy transitions and climate change mitigation. Digital applications can improve the energy efficiency of processes and systems, but can also induce demand for energy-hungry activity. Digital infrastructure like data centres also has a large energy footprint. AI is amplifying and accelerating these impacts, both for better and for worse. How does this affect the feasibility of long-term climate targets? We use a global integrated modelling framework to quantify scenarios to 2050 describing alignment or misalignment between digitalisation and climate goals. We combine empirical estimates of the energy impacts of digital and AI applications in buildings, transport, and industry sectors with digitalisation’s role in the integration of renewables, storage, and demand flexibility in electricity systems. We show digitalisation uncertainties for future energy demand and CO 2 emissions are large - similar in magnitude to the effect of all global demographic, economic, and technological uncertainties quantified in future climate pathways. We also show that available near-term assessments of AI impacts within this uncertainty space are optimism biased. Energy transition and emission risks from digital and AI applications in transport and buildings are a factor five of higher than from data centres and other infrastructure. Climate policy is the most effective driver of emission reductions to meet Paris targets but climate-aligned digitalisation reduces energy investment needs by a factor of two down to $35 trillion and brings the global net-zero year forward to as early as 2047. Conversely, climate-misaligned digitalisation drives up energy demand and sees lost opportunities to build a reliable and renewable future electricity system. Resulting increases in near-term cumulative CO 2 emissions mean the probability of limiting warming to 2°C almost falls below the ‘likely’ threshold, increasing risk of temperature overshoot. Digital transformation can undermine or enable the feasibility and affordability of long-term climate targets delivered by decarbonised energy systems. Entwining digital and low-carbon energy transitions requires strategic governance and institutional innovation to link climate and digital concerns.

Strategy planning for global climate goals requires structured, multisectoral data linking environmental pressures with socioeconomic drivers across time and geography. However, internationally harmonized, machine-actionable datasets integrating waste generation, waste-related greenhouse gas (GHG) emissions, and socioeconomic indicators remain scarce. This study provides a harmonized, AI-ready dataset to support global analyses of municipal solid waste (MSW) and associated emissions. This FAIR2 dataset provides historical (1990–2020) and forecasted (2021–2050) national-level data for 43 countries, covering MSW generation, CO2, CH4, and N2O emissions, GDP per capita (PPP), and population. Forecasts were generated using an ensemble of fixed-effects regression models and artificial neural networks informed by economic and demographic trends. By linking MSW, emissions, and socioeconomic drivers within a standardized structure, the dataset enables analyses including benchmarking, equity assessments, and decoupling analysis. While limited to national aggregates and subject to scenario uncertainty, the dataset complies with FAIR2 principles, supporting reuse and traceability.

There has been plenty of research on the influence of various socio-economic and demographic data on waste generation to develop effective and targeted waste reduction measures, including energy recovery. This study evaluates the relationship between the waste generation and Circular Material Use rate, Environmental Tax Revenue, and Global Innovation Index beyond the typical socio-economic factors (e.g., gross domestic product or population). Correlation analysis is conducted on the EU-27 datasets before the development of the predictive model. The correlation strength between the factors is discussed to identify the potential rebound effect from the central driver of economic growth and development. A positive correlation and partial rebound effect are identified in the data. The waste amount ending in disposal and energy recovery treatment increases with the Circular Material Use rate, suggesting that the expected gains from Circular Material Use rate are offset by other socio-economic factors such as increasing population or gross domestic product. However, a diminishing trend is observed in the rebound effect over the years. Multiple linear regression with validation is applied to identify the best fit model for predicting waste generation. Using population, gross domestic product, Circular Material Use rate, and Environmental Tax Revenues as independent variables, a model is generated with a mean absolute percentage error of 18.65% (7% lower than the benchmark) and R-2 (coefficient of determination) of 0.995.

Integrating heat pumps into large-scale electricity-to-heat industrial processes has proven highly successful in enhancing the utilisation of renewable energy and contributing to carbon emission reductions. However, most studies focus on overall system performance, overlooking the detailed thermal behaviour of the heat pump itself. This limits the adaptability of heat pumps in dynamic industrial settings. This work proposes an equation-oriented framework that enables flexible integration of thermodynamically detailed heat pump models into industrial heat recovery systems. A superstructure-based optimisation model is developed to minimise energy costs and enhance efficiency, considering process constraints, network layout, and heat pump performance. The model dynamically optimises heat pump operation and placement to enhance waste heat recovery and overall system integration. Moreover, the approach supports the integration of low-grade utilities to further improve the energy efficiency. The proposed framework is validated through an industrial-scale case study of a crude oil distillation process. Life cycle assessment is conducted to quantify potential environmental and economic benefits. Results show that integrating heat pumps into the system recovered 50.52 % of low-pressure steam, reducing the total operating cost and annual cost by 12.88 % and 12.42 %. Additionally, total net carbon emissions decreased by 28.70 %. Lower electricity prices increase heat pump use and economic benefits but also amplify rebound effects. Furthermore, although high-temperature heat pumps operating above 150 degrees C tend to increase capital expenditures, they unlock greater energy efficiency, thereby accelerating the industrial decarbonisation process.

The mechanism of environmental regulation on energy conservation and carbon reduction in the petrochemical industry through directed technological progress remains uncertain due to the directional characteristics of technology. This paper develops a mechanism framework and employs a panel two-way fixed-effects model to clarify the impact of environmental regulation on directed technological progress and energy conservation, while uncovering its underlying mechanisms. Subsequently, a dynamic Kaya model is constructed, using the Monte Carlo method to determine the required intensity of environmental regulation for China's petrochemical industry to actualize the SSP1-CHN, SSP1, and SSP2 scenarios. The model also simulates the future bias of technological progress, energy utilization, and potential carbon emissions under each scenario. The findings indicate that increasing the intensity of environmental regulation drives technological progress toward energy conservation, thereby enhancing energy-saving biased technological progress, improving energy productivity, and optimizing the energy structure. Furthermore, to actualize the carbon peak by 2030 and carbon neutrality by 2060 under the SSP1-CHN scenario, the annual growth rate of environmental regulation intensity in China's petrochemical industry should be no less than 8 % before 2030 and should be strengthened to 20 % after 2030.This study not only extends the application of directed technological progress theory in the energy field but also provides innovative and practical environmental policy recommendations for the low-carbon development of the global petrochemical industry.

While the pay-as-you-throw approach is widely adopted, some emerging megacities have successfully implemented incentive-based schemes to encourage residents to participate in waste recycling. A common incentive is rewarding residents based on the value of recyclables (e.g., monetary incentives or accumulated points used for gift exchange). However, monetary incentives are subject to fluctuations in recycling market prices, which may further influence residents' recycling behavior. To evaluate whether such price fluctuations will affect residents' willingness to participate in household waste recycling in emerging megacities, it is crucial to examine residents' sensitivity to the price of recyclables. In this study, we investigated residents' preferences for different incentives and residents' price sensitivity of incentive-based recycling of household waste by building models on preference heterogeneity analysis and price sensitivity measurement (PSM). Analysis results based on first-hand data from two emerging megacities yield several findings. First, residents exhibit a stronger preference for monetary incentives and practical items (e.g., daily necessities and groceries). Second, preferences for reward types vary across emerging megacities (e.g., different preferences shown for entertainment products and subsidies incentives), implying that a one-size-fits-all incentive scheme is not effective among cities. Third, fluctuations in the pricing of recyclables do influence the willingness to participate in recycling. However, it is essential to ensure the price adjustments do not devalue the perceived worth of recyclables. The quantitative analysis suggests that megacities like Shanghai and Chengdu should not reduce the price by over 21.15 % and 13.74 %, respectively. These new findings could provide policy-relevant insights to stakeholders in the household waste recycling industry of emerging megacities.

Circular economy is recognized as one of the most effective strategies for promoting plastic sustainability. However, its implementation requires to enhance consumer engagement, which remains a primary target of regulatory initiatives designed to promote plastic circular economy. To ensure sustained consumer participation, it is essential to evaluate and optimize various incentives, including regulatory policies, voluntary programs, and market-related mechanisms. This study applies Stackelberg Game Approach to quantitatively capture the strategic interactions between the authorities (as the leader) and consumers (as followers). The model incorporates key consumer behaviors, i.e., "use less," "use longer," and "recycling", to reflect their role in advancing plastic circular economy goals. By integrating factors such as governmental utility (gains of benefits), consumer utility (welfare), and plastic waste reduction, the model identifies the optimal intensities of various public initiatives, which represent the quantitative levels of policy intervention necessary to maximize desired outcomes. A case study based on EU-27 data demonstrates the model�s applicability, revealing optimal intensities of 0.91, 0.41 and 0.8 for regulatory, voluntary, and market-related initiatives. At these optimal intensities, regulatory measures achieve the highest governmental utility (benefits) and plastic waste reduction, while market-related mechanisms yield more favorable outcomes for consumer welfare. The findings highlight the need for a balanced policy mix that effectively aligns government objectives with consumer incentives.

Digital transformation refers to the widespread use of digital technologies in ways that reshape societal and economic activity, with significant impacts on sustainable development and climate challenges—both for better and for worse. Using statistical models calibrated to historical evidence in 62 countries across 12 world regions, we project future digital transformation within the Shared Socioeconomic Pathways (SSPs), adding contextual richness to this scenario framework used extensively in global climate research. In some scenarios, we find a pervasive and prolonged digital divide with up to 45% of the assessed population by mid-century still residing in countries with relatively low levels of digital transformation despite ever-deepening digitalisation in wealthier countries. We set out six use cases for how our explicit representation of digital transformation within the SSPs enables quantitative assessment of digitalisation’s impact on energy, emissions, climate policy, and Sustainable Development Goals. We also discuss challenges with using empirically calibrated models to project digital transformation given its rapid evolution and socioeconomic implications.

The pressing challenge of persistent air pollution and greenhouse gas emissions, which contribute to global boiling beyond global warming, requires urgent solutions across all sectors. In the transportation sector, zero-emission electric vehicles (EVs) are increasingly recognized as a key strategy for achieving carbon neutrality. However, the competitiveness of EVs is constrained by limitations in charging infrastructure and charging time. To address these challenges, this study focuses on optimizing the location of EV charging stations in Seoul for the year 2030, considering the existing fast charging stations and gas stations as of 2023. We use a genetic algorithm (GA) combined with a fuzzy analytic hierarchy process (Fuzzy AHP) to identify optimal locations for charging stations, while reorganizing the ratio of fast to slow chargers within these stations to alleviate road congestion and reduce unnecessary trips. Our methodology integrates various urban and transportation metrics, including parking index, public transit connectivity, and land use plans, to refine this optimization process. Our findings suggest that retaining 63