Environmental and Earth Sciences

Abstract: Territorial space use efficiency (TSUE) is a core indicator characterizing regional sustainable development. Nevertheless, existing studies have not established a systematic quantitative system to assess sustainable development status based on TSUE. From the perspective of production-living-ecological spaces, this study constructs a multi-dimensional efficiency evaluation framework for sustainable territorial management. This study adopts geographic data envelopment analysis (GeoDEA), spatial autocorrelation analysis and multi-scale geographically weighted regression (MGWR). It calculates TSUE, uses efficiency levels to characterize and identify territorial sustainable development status, and further reveals the spatiotemporal evolution characteristics and driving mechanisms of regional sustainable development. The results indicate that the regional sustainable development level reflected by TSUE generally shows fluctuating evolution, with distinct phase transitions emerging around 2010. Merely 20% of cities attain medium-high or higher sustainability levels, and spatial imbalance is prominent nationwide. Areas with high-efficiency production and living spaces are mainly clustered in eastern and central China, while highly sustainable ecological spaces concentrate in western and northeastern China. In addition, driving factors exert significantly heterogeneous effects on the three types of territorial functional spaces. This study provides scientific references for implementing differentiated territorial spatial governance and advancing sustainable territorial development.

Abstract: Extreme heat is an escalating climate-health hazard in the United States. However, its health effects remain uneven because exposure intersects with housing quality, energy insecurity, chronic illness, outdoor work, transportation barriers, limited tree canopy, and historical disinvestment. This study combines an integrative evidence synthesis with an exploratory ecological secondary analysis of publicly reported Centers for Disease Control and Prevention data for the 10 U.S. Department of Health and Human Services regions. The analysis examined regional mean warm-season 2023 heat-related illness emergency department (HRI ED) visit rates, 2018–2022 baseline rates, and the number of 2023 days above each region’s historical 95th percentile. Elevated day counts and 2023 regional rates were strongly correlated (Pearson r = 0.874, p = 0.001; Spearman rho = 0.924, p < 0.001), as were baseline and 2023 rates (Pearson r = 0.938, p < 0.001). Conventional ordinary least squares models showed strong associations, but heteroscedasticity-robust estimates and influence diagnostics indicated that the adjusted elevated-day coefficient was unstable in this small sample. The analysis therefore demonstrates regional clustering in the surveillance measures rather than causal or independently predictive effects. The evidence synthesis supports a five-pillar framework linking hazard anticipation, social vulnerability mapping, targeted intervention, adaptive risk communication, and ethical governance. Artificial intelligence can strengthen heat-risk management when it is locally validated, transparent, privacy-protective, and connected to funded interventions such as functional cooling, energy assistance, worker protections, transportation, wellness checks, resilient housing, and urban heat mitigation. AI should operate as accountable decision support rather than replace operational meteorology, public-health expertise, or community knowledge.

Abstract: Hydrothermal pretreatment is widely proposed to improve the anaerobic digestibility of lignocellulosic biomass, yet whether it repays its own energy input is rarely quantified. We compare microwave and conventional (conductive) acid-assisted thermohydrolysis of willow (Salix viminalis) and maize silage at 110-130 °C, coupling biochemical methane potential (BMP, n = 3) with an incremental energy balance against an untreated control. Substrate, heating mode and temperature were all significant (p < 0.001), heating mode dominating. Maize silage responded monotonically, reaching 306.0 ± 4.0 NmL CH4 g⁻¹ VS at 130 °C under microwave heating (+31.4%), whereas willow behaved erratically: its best variant reached 310.6 ± 2.5 NmL CH4 g⁻¹ VS (+28.9%), yet two conductively heated variants fell below the control. Furanic by-products stayed at trace levels, well below inhibitory thresholds, excluding toxicity. Critically, no variant recovered its own energy input: every treatment was net-negative, the least unfavourable being microwave heating at 110 °C (-3.57 and -3.48 kJ g⁻¹ DM). Heating mode changed the penalty three- to fivefold, microwave being superior in all six paired comparisons. Under the mild conditions tested, acid-assisted thermohydrolysis is not self-financing in energy terms; where applied for other reasons, microwave heating is the only defensible option.

Abstract: The global carbon footprint of urea production exhibits substantial variability, hindering comparative assessments and decarbonization strategies in agricultural supply chains. This study identified and quantified the structural determinants driving this dispersion by synthesizing an international inventory (n = 60) combining Life Cycle Assessment databases, literature, and empirical industrial data. Methodologically, an extreme theoretical outlier (71,420 kg CO₂-eq/t urea) was isolated, and a refined dataset (n = 59) was evaluated using one-way ANOVA, Tukey's HSD test, and Ward's hierarchical clustering. Statistical analysis confirmed that a five-category technological typology—Coal, Mixed Systems, Average Gas, Efficient Gas, and Green Urea—is highly robust (F(4, 54) = 167.79; p < 0.001), with technology explaining 92.8% of global emission variance (η² = 0.9281). Mean impacts ranged from 2,735 kg CO₂-eq/t for coal to 334 kg CO₂-eq/t for green urea. Primary data from an Argentine plant (777.8 kg CO₂-eq/t cradle-to-gate) defined a practical lower bound for fossil systems, while commercial operations cluster within a baseline of 1,100–1,500 kg CO₂-eq/t. We conclude that urea carbon intensity is governed by feedstock technology and life-cycle accounting choices, providing an essential quantitative framework for inventory harmonization.

Abstract: Photovoltaic (PV) tilt optimization is commonly guided by latitude-based rules, but these heuristics do not explicitly account for sub-daily irradiance variability, diffuse-fraction behavior, or local atmospheric attenuation. This study presents FT-PVOT, a geometry-resolved framework that integrates National Solar Radiation Database (NSRDB) irradiance data with solar-position and plane-of-array (POA) transposition equations to identify irradiance-maximizing fixed and seasonal PV tilt angles. Direct normal irradiance, diffuse horizontal irradiance, and global horizontal irradiance were evaluated using a brute-force tilt sweep from 0° to 90° at 1° increments. The method was tested for Gainesville, Florida (29.65° N), using 2018-2023 NSRDB data and benchmarked against PVWatts tilt trends. The annual fixed optimum remained highly stable across the six-year period, ranging from 28° to 29° with a mean of approximately 28.8° and a standard deviation of approximately 0.41°. PVWatts produced an annual optimum of 29°, yielding a mean difference of approximately 0.17°. Relative to flat mounting, latitude tilt increased annual POA irradiation by approximately 9.2%, annual optimization by 9.6%, biannual adjustment by 13.5%, and monthly adjustment by 15.1%. However, monthly adjustment added only 1.6 percentage points, or approximately 27 kWh/m²/year, beyond biannual adjustment. Cloudy-sky conditions reduced annual POA irradiation by approximately 35.5% relative to the clear-sky case, but the annual optimum fixed tilt remained approximately 29°. These results show that high-resolution irradiance integration can convert latitude-based tilt guidance into a quantified, reproducible, location-specific design recommendation while preserving a clear distinction between irradiance optimization and full PV electrical-output prediction.

Abstract: Chronic diseases represent one of the major challenges to global public health due to their high prevalence, morbidity, mortality, and socioeconomic impact. Growing evidence indicates that environmental exposures, in addition to genetic and behavioral factors, play a significant role in their onset and progression. This review addresses the mechanisms linking environmental pollutants, endocrine disruption, epigenetic alterations, and developmental biological programming to the emergence of chronic diseases, as well as the potential of nanotechnology for their diagnosis, monitoring, and treatment. The literature analyzed demonstrates that environmental contaminants can induce oxidative stress, chronic inflammation, mitochondrial dysfunction, and epigenetic reprogramming. During critical periods of development, these alterations may influence future susceptibility to cardiovascular, metabolic, respiratory, neurodegenerative, and neoplastic diseases, as described by the Developmental Origins of Health and Disease (DoHaD) theory. Furthermore, nanomaterials and nanostructured systems present promising applications in precision medicine, biomarker detection, targeted drug delivery, and environmental remediation. Collectively, these findings highlight the importance of integrated strategies that combine nanotechnology, environmental health, sustainability, and the One Health concept to reduce environmental risks and promote human, animal, and environmental health.

Abstract: The tourism industry faces increasing pressure to provide highly personalized experiences while maintaining environmental sustainability. In this context, recent advances in generative artificial intelligence and large language models offer new opportunities to develop systems capable of assisting travelers in planning more environmentally responsible trips. However, the effective integration of these technologies into practical tools for sustainable tourism planning remains an emerging challenge. This paper presents the design and evaluation of an intelligent conversational agent for sustainable tourism planning, developed in an experimental environment at Technology Readiness Level (TRL) 4. The system integrates large language model based conversational capabilities with external tourism information services to generate personalized travel itineraries through natural language interaction. The proposed architecture interprets user preferences and produces structured itineraries including transportation, accommodation, and activities while incorporating sustainability criteria such as carbon footprint considerations. The study demonstrates the feasibility of combining conversational AI with dynamic travel information retrieval to support sustainable tourism planning, while highlighting challenges related to heterogeneous data integration, environmental impact estimation, and real world deployment contexts. Overall, the findings provide evidence of the potential of AI based conversational agents to support hyper personalized and environmentally responsible travel planning, establishing a foundation for future research and development toward practical intelligent travel assistance systems.

Abstract: Afforestation can mitigate the export of water, sediment, and dissolved or adsorbed contaminants to river systems, but identifying effective intervention sites requires accounting for multiple flow-related criteria and their spatial interactions. This paper presents a multi-criteria heuristic approach that extends CAMF (Cellular Automata-based Heuristic for Minimizing Flow), originally designed to select cells from a rasterized landscape for interventions that minimize sediment yield at target sites. We integrated the Distance-to-Ideal-Point (DIST2IP) algorithm in CAMF, enabling the selection of cells where intervention can minimize two or more flows simultaneously. The multi-criteria CAMF was applied ex post to the radioactively contaminated Niida river catchment, Fukushima prefecture, Japan, to identify 1,000 cells within decontaminated zones where immediate afforestation would have maximally reduced both sediment and residual ¹³⁷Cs export. The 1,000 best cells selected by DIST2IP, representing 4% of the decontaminated cells, would have reduced sediment export by 22% and ¹³⁷Cs export by 6%. Selected cells are within the union of cells identified by the two single-criteria optimizations and are predominantly close to water bodies, confirming that blocking flow paths before they connect to the river system is most effective.

Abstract: The sustainability of territorial agri-food systems depends on whether high-value produce can reach a paying market. The yam supply chain of Montes de María, a post-conflict subregion of the Colombian Caribbean, sorts its product into a non-substitutable export grade and a local grade. This study assesses the structural resilience of the chain and its food-security implications by integrating network analysis with a household survey of the territory. The chain is modeled as a weighted directed graph in three layers: value concentration, structural criticality via Menger edge connectivity, and a differential-equation model of export-grade erosion under a demand shock with saturating absorption and storage; a parallel food-insecurity and dietary-diversity survey sets the baseline. Results, reported as conditional scenarios, show the export value hangs on a single non-redundant channel: one cut disconnects the export outlet. Under a shock, permanent loss nears 18% of the diverted product versus 50% without storage, while the export-income collapse, about 40% or 57% when severe, is not buffered. With 41% of surveyed households food-insecure, this fragility erodes rural livelihoods through economic access. Diversifying the export outlet and strengthening local storage emerge as levers for a more resilient and sustainable chain, supporting food security and territorial peace.

Abstract: Banana peels constitute a major agro-food residue that is frequently discarded despite their potential as a valuable substrate for the production of value-added products. This study evaluated the feasibility of producing artisanal vinegar from discarded banana peels collected at the Cerâmica Wholesale Market in Beira, Mozambique, and characterized the physicochemical and sensory properties of the resulting product within a circular economy framework. Banana peels underwent spontaneous alcoholic and acetic fermentation over 60 days under ambient conditions, followed by physicochemical analyses and sensory evaluation. The raw material exhibited high moisture content (78.6%) and adequate reducing sugar concentration (9.8 g/100 g), confirming its suitability for fermentation. The process achieved a vinegar yield of 92.4%, while pH decreased from 5.82 to 3.92 and total acidity increased to 4.30% acetic acid. The final product complied with reference quality standards for food-grade vinegar regarding acidity, pH, density, dry extract, and ash content, and obtained sensory scores above 7.0 for all evaluated attributes, indicating good consumer acceptance. These findings demonstrate that discarded banana peels can be successfully transformed into high-quality artisanal vinegar through a simple and low-cost process, supporting waste valorization, circular economy principles, sustainable waste management, and income-generation opportunities in developing countries.

Abstract: Per- and polyfluoroalkyl substances (PFAS) are synthetic fluorinated compounds widely recognized for their stability and persistence in the environment. These characteristics make PFAS valuable in many applications but also pose serious health risks because they do not break down easily. These chemicals can accumulate in living organisms and persist in water systems. PFAS typically transfer from water to other media rather than being completely degraded by conventional treatment technologies such as adsorption and membrane filtration. Chemical degradation techniques, i.e., advanced oxidation processes or electrochemical conversions, require harsh reaction conditions, which make them unsustainable. Microbial degradation offers a green, sustainable alternative for PFAS remediation in water. This review critically analyzes advances in the use of bacterial, fungal, and microbial consortia for PFAS transformation via reductive and oxidative defluorination and/or metabolic reactions. Later, the influence of PFAS structural attributes, i.e., chain length and functional head groups, on microbial activities has been discussed. In the following section, the current progress toward the complete mineralization of PFAS is evaluated. Considering the critical evaluation of microbial degradation processes, several research gaps have been identified, including the lack of detailed mechanistic studies of enzymatic degradation pathways, the need to optimize microbial systems for the sustainable degradation of PFAS, and the integration of biological approaches with other technologies to achieve complete PFAS mineralization.

Abstract: Power Grid Projects (PGPs) are pivotal to energy transition, yet their complex engineering structures hinder precise carbon quantification. This study proposes a modular carbon emission accounting and evaluation framework for different types of PGPs. By deconstructing projects into 11 typical modules, we establish a modular-lifecycle that aligns physical engineering logic with carbon characteristic extraction. The research develops an automated method-matching engine and utilizes an Improved Particle Swarm Optimization-Deep Reservoir Echo State Network (IPSO-DRESN) model to determine modular carbon quotas with high precision. Empirical analysis reveals that the total lifecycle footprint of 500kV PGP is 13,569.1 tCO2e. The Operation and Maintenance (O&M) phase is the dominant emission source (54.4%), while the Production and Construction (P&C) phase (38.2%) is characterized by intense mechanical energy consumption. This research further establishes a low-carbon retrofit evaluation system, identifying high-capacity conductors and low-loss transformers as the most “carbon-elastic” interventions. By transforming fragmented engineering data into standardized modular quotas, this study provides a rigorous scientific tool for utility managers to implement lifecycle-based carbon benchmarks and optimize decarbonization strategies in the power sector.

Abstract: The rapid acceleration of the global climate crisis poses an existential threat to food security, environmental hygiene, and global public health. Traditional agricultural frameworks lack the predictive granularity required to withstand hyper-local abiotic fluctuations and systemic eco-toxicity. This paper establishes a novel computational and physical paradigm—IoT-Enabled Precision Epigenomics—operating at the intersection of Agriculture 5.0 and the One Health mandate. We present a hybrid deep learning architecture uniting Convolutional Neural Networks (CNNs) for spatial genomic/epigenomic motif extraction with Long Short-Term Memory (LSTM) Recurrent Neural Networks for temporal environmental stress-memory modeling. By feeding real-time telemetry from Internet of Things (IoT) field sensor matrices into this network, the system decodes and predicts site-specific plant epigenetic modifications (such as DNA methylation and histone acetylation) before physical phenotypic degradation occurs. Furthermore, this intelligence layer is physically coupled with automated robotics and decentralized via blockchain ledgers to secure data integrity. We evaluate this system across three integrated deployment domains: climate-resilient delta agro-ecosystems, environmental contaminant tracing (PFAS and microplastics tracking), and occupational hazard mitigation. Finally, we address critical sociotechnical barriers, including farmers' adaptation behaviors, ethical constraints, and algorithmic governance. Complete programmatic architectures for rendering the methodology models using Python are provided to ensure open-source reproducibility.

Abstract: This study examines whether organoponics-based mixed farming can provide a viable low-carbon pathway for improving productivity, profitability, and drought resilience in dryland smallholder systems in Matabeleland South, Zimbabwe. Using a quasi-experimental action-research design, the study compared tomato production in a tropical greenhouse organoponics system with that of open-field cultivation. It also assessed complementary maize organoponics and cattle pen-feeding practices. A renewable-energy irrigation scheme supplied water through a drip system, enabling controlled production under water-constrained conditions. The findings show that the greenhouse system substantially outperformed open-field cultivation in plant growth, health, yield, and commercial return, with first-cycle profit reaching $2,380 and later cycles projected to exceed $11,000 each under similar operating conditions. Maize intervention also demonstrated strong production and market potential, particularly when sold as green mealies. The livestock results showed that early and consistent pen-feeding reduced drought-related herd losses. The study concludes that integrated organoponics-based mixed farming can strengthen food security and economic resilience in dryland environments, but wider adoption will depend on targeted financing, farmer training, infrastructure, and improved market access.

Abstract: The contradiction between economic growth and carbon emissions restricts the sustainable development of developing countries. Whether territorial spatial functional conflicts (TSFCs) are statistically associated with the intensification of this tension remains an important empirical question that has not been fully addressed. This study comprehensively employs multiple spatiotemporal analysis methods to analyze the spatiotemporal association between TSFC intensity and the synergy state of economic growth and carbon emission in Sichuan Province, China, over the period 2010–2022. The results show that the territorial spatial functional conflict index (TSFCI) level has significant explanatory power for the spatial heterogeneity of both the CCD and the decoupling elasticity index (DEI) between economic growth and carbon emission, with q-values of 0.081 and 0.023, respectively, both passing the significance test at p &lt; 0.001. The time-stratified geographical detector results further reveal that the explanatory power of TSFCI for DEI reaches significant levels across all four periods (2010–2013, 2013–2016, 2016–2019, and 2019–2022), with q-values of 0.254, 0.065, 0.118, and 0.030, respectively, exhibiting pronounced phased fluctuations. These findings demonstrate, from both spatial and temporal dimensions, a substantial association between TSFCs and the synergy between economic growth and carbon emission, with the association characterized by phased fluctuations and nonlinear features. These results provide spatiotemporal empirical evidence for the close linkage between TSFCs and economic-carbon contradictions, while the causal inference that TSFCs exacerbate the contradiction is supported only by theoretical mechanism deduction rather than direct empirical proof. Further analysis suggests that the territorial spatial governance approach of alleviating the economic-carbon contradiction by coordinating TSFCs and optimizing territorial spatial development and conservation patterns is theoretically plausible. This study provides empirical reference and theoretical implications for developing countries to mitigate the contradiction between economic growth and carbon emission by optimizing territorial spatial development and conservation patterns.

Abstract: The sustainable management of stingless bees is fundamental for biodiversity conservation, food security, and the ecological balance of terrestrial ecosystems. Nevertheless, technological solutions for monitoring native species, such as Tetragonisca Angustula (Jataí bee), remain scarce, often relying on manual, traditional bee management methods that may induce stress and reduce productivity. Although Apis Mellifera management has been equipped with Internet of Things (IoT) solutions for smart beekeeping, there are no commercial solutions to support the smart monitoring of native species. The purpose of this research is to develop a conceptual design for smart beekeeping for native species, considering economic, social, environmental, and scientific issues. Considering this range of intentions, the research team opted for making a comprehensive bibliometric analysis of global research integrating embedded systems, the Internet of Things, and artificial intelligence (AI) into sustainable beekeeping and meliponiculture as a starting point. Data were collected from the Scopus, Web of Science, and IEEE Xplore databases, covering publications from 2012 to 2024. A total of 237 articles were retrieved, and 41 were selected for in-depth analysis using the Bibliometrix and Biblioshiny tools. Results reveal a rising global interest in precision beekeeping, with Brazil and Malaysia emerging as leading contributors. The major thematic clusters were identified, and a set of key articles and books on smart beekeeping was reviewed to adapt the concept to stingless bees. The requirement list pointed to humidity, temperature, sound, and weight monitoring, but also to population counting by small cameras from the outside that also helps to understand defensive, pollen, and nectar search, and intrusion analysis in the meliponiculture practice for different stingless bee species. A framework for the conceptual design was built considering the main commercial Brazilian stingless bee specie.

Abstract: (1) Background: Climate change is reducing the climatic windows in which outdoor sport and physical activity can be safely practised. Yet it remains unclear whether this emerging burden is distributed evenly across countries, or whether it is concentrated on populations with lower adaptive capacity. Here we assess the country-level distribution of sport-relevant heat stress along the socio-economic vulnerability gradient. (2) Methods: Within an explicit hazard--exposure--vulnerability framework, we combine reconstructed afternoon Wet-Bulb Globe Temperature (WBGT) exceedance from bias-corrected CMIP6 projections (NEX-GDDP, five-model ensemble) with SSP-consistent gridded population and a published national vulnerability index (GVI). Two internally coherent futures (SSP1-2.6 and SSP2-4.5, each paired with its corresponding socio-economic pathway) are evaluated at mid-century (2055) and late century (2085). Population-weighted exposure is aggregated at country level, and its concentration along the vulnerability gradient is quantified using a concentration index (CI). (3) Results: The exposure burden is concentrated on more vulnerable countries in all scenario--horizon--threshold combinations (CI $>$ 0; 0.09–0.25). At mid-century, heat severity further amplifies this inequity: under SSP1-2.6 in 2055, the CI rises from 0.17 for WBGT \(\geq\) 28~$^\circ$C to 0.25 for WBGT \(\geq\) 32~$^\circ\(C, indicating that the most severe sport-relevant heat is disproportionately located in vulnerable countries. By 2085, about 4.5 billion people live in cells experiencing at least 30 days yr\)^{-1}$ with WBGT \(\geq\) 32~$^\circ$C under SSP2-4.5, compared with 2.3 billion under SSP1-2.6. (4) Conclusions: The climatic erosion of safe outdoor sport and physical activity is structurally inequitable. The sustainable pathway reduces both heat hazard and population exposure, highlighting the joint importance of mitigation, development and equity-aware adaptation for preserving access to safe outdoor activity.

Abstract: The present study evaluated the technical feasibility of the artisanal production of eco-friendly paper from sugarcane bagasse discarded at the Cerâmica Wholesale Market in Mozambique. Sugarcane bagasse, rich in cellulose, hemicellulose, and lignin, repre-sents an agro-industrial residue with high potential for reuse within the principles of the circular economy. The production process was carried out through soda pulping using sodium hydroxide (NaOH), followed by washing, grinding, molding, and natural drying of the sheets. The produced samples were analyzed in triplicate for grammage , moisture content, and pH. The results showed an average grammage of 91.67 ± 0.43 g/m², indicat-ing suitable density and potential for artisanal applications and lightweight packaging. The average moisture content was 25.38 ± 0.28%, suggesting high water retention associ-ated with natural drying and fiber porosity, while the average pH of 8.72 ± 0.14 character-ized the material as slightly alkaline and potentially more resistant to aging. These find-ings confirm the feasibility of using sugarcane bagasse as a raw material for eco-friendly paper production, contributing to agro-industrial waste reduction and the sustainable valorization of local resources. Further improvements in the drying process and comple-mentary analyzes are recommended to optimize paper quality.

Abstract: Monthly cane-supply anticipation is critical for harvest scheduling, mill-intake coordination, transport allocation, labor planning, and maintenance organization in vertically integrated sugarcane agroindustrial systems. This study developed an interpretable monthly decision-support framework using twelve years of original institutional records from Compañía Azucarera Valdez S.A., Milagro, Guayas, coastal Ecuador, covering January 2007 to December 2018. The primary endpoint was one-month-ahead monthly cane tonnage, predicted from variables available at the current monthly time point to avoid look-ahead bias. Complementary diagnostic layers were used to characterize recurrent operational states and to interpret monthly energy-saving behavior. A chronological validation design was applied, with 2007–2015 used for model training and 2016–2018 used for independent testing. The full linear and LASSO models achieved the strongest test performance, with R² values of 0.916 and 0.915 and mean absolute percentage errors near 5%, outperforming random forest and ANN/MLP benchmarks. The area-only baseline was weaker, while the no-area model retained predictive capacity, indicating that harvested area was important but insufficient to explain monthly cane tonnage. PCA and k-means clustering identified four recurrent operational states related to production scale, stress, crop quality, and energy-performance conditions. Monthly energy saving showed ceiling-constrained behavior near 40% and was therefore interpreted as a diagnostic indicator rather than a robust forecasting target. Overall, the framework supports transparent monthly planning by combining leakage-aware forecasting, operational-state interpretation, and conservative energy-performance diagnostics.

Abstract: Despite its long history, bioenergy is far below its potential to deliver more sustainable and inclusive prosperity across the globe. This report extends lessons from California energy and climate policy to show how this can be achieved. Greater attention to circular bioeconomy pathways, including biomass conversion into clean energy and carbon sequestration products, can address fundamental climate, environmental, and socio-economic challenges worldwide.