Climate Change Mitigation Research Articles

Variable associations of annual biomass increment with age, latitude and germination year in four tree species in Sweden

Forests are pivotal for biodiversity, food webs, and human economies, and as carbon sinks their climate change mitigation potential is undisputed. However, whether trees continue to efficiently accumulate biomass at an increasing rate with age, how growth trajectories respond to climate change, and vary with latitude at species range margins is under debate. Here, we combine tree-ring data with biometric equation modeling to analyze how annual biomass increment varies according to tree age, species, latitude, and germination year. We generated 26,225 estimates of annual biomass increments for 136 individual trees, representing two evergreen gymnosperm conifer species (Picea abies (L.) H.Karst., and Pinus sylvestris L.) and two deciduous angiosperm species (Quercus spp. L. and Fagus sylvatica L.). Our dataset includes some old trees (range 150 - 405 years), and samples from locations spanning >10° latitude (55.0 - 66.3 °N) in Sweden. Annual biomass increments varied considerably between species, years and among individual trees within stands. On average, biomass increment remained positive throughout the lifespan across trees, species, and latitudes. Age-specific biomass increment was higher in the deciduous than in the evergreen species and declined with increasing latitude within species. For spruce and beech, biomass increment increased significantly with germination year, possibly reflecting faster growth in recent times in response to a warmer climate. The findings have implications for forestry practices aimed at productivity, biodiversity conservation, and climate change mitigation.

Carbon Sequestration Potential of Rhizophora mucronata in Tongke-Tongke Mangrove Forest, Sinjai Regency, Indonesia

Mangroves play a crucial role in coastal ecosystems, offering significant ecological benefits and acting as important carbon sinks. This study focuses on the carbon sequestration potential of Rhizophora mucronata in the Tongke-Tongke mangrove forest, Sinjai Regency, Indonesia. By measuring the carbon content in leaves, roots, and sediments, the study found carbon content ranges from 0.09 to 0.11 tons/ha in leaves with a carbon absorption capacity ranging from 0.33–0.42 tonnes/ha and 0.44 to 0.72 tons/ha in roots with absorption capacity reaching 1.60–2.64 tonnes/ha, with sediment carbon content varying significantly ranges from 8.66–156.83 tons/ha with an absorption capacity of 31.79–575.55 tons/ha. The carbon content in mangrove stands reaches 109.23–180.87 tonnes/ha, with carbon uptake reaching 400.89–663.80 tonnes/ha. Results indicate that R. mucronata has a substantial carbon absorption capacity, underscoring the importance of these ecosystems in climate change mitigation. The findings aim to inform conservation strategies and potential carbon trading initiatives, contributing to global climate resilience.

Contrasting forest management strategies: Impacts on biodiversity and ecosystem services under changing climate and disturbance regimes

Natural disturbances may compromise the past and ongoing efforts to increase carbon sequestration and halt biodiversity loss in boreal forests. Measures to minimize the effects of forest disturbances i.e., adaptive management, offer solutions to secure future timber yields. However, the consequences of adaptive management on biodiversity, the climate change mitigation potential of forests, and other ecosystem services are not well understood. In addition, the impact of climate change and disturbances on future forest-based mitigation potential is not well known. We compared the effects of forest management options emphasizing climate change mitigation or adaptation on boreal forests in changing climate and disturbance regimes in southern Finland. We used the process-based forest landscape and disturbance model iLand to dynamically model interactions between climate change and disturbances together with forest management and protection options, and examined the consequent effects on forest carbon storage, berry yields, recreation, and structural attributes important for biodiversity. Mitigation managements resulted in up to one-fifth higher carbon stocks, even after accounting for disturbances by wind and bark beetles, but halved annual harvests over the 80-year simulation period. Adaptive managements reduced bark beetle disturbances, but in some cases the disturbed volumes were even higher than under business-as-usual management due to increased wind damage. The effects of proactive risk management depended on the time horizon considered, the adaptive management option chosen and the climate change scenario. In general, the mitigation managements had positive effects on the biodiversity indicators studied, while the effects of adaptive management were mixed. Our results highlight the complex interactions between disturbance risk prevention, biodiversity, carbon sequestration and storage, and other ecosystem services. The results guide forest managers and policymakers to plan mitigation and adaptation strategies optimizing multiple benefits, and strengthening forest resilience in a changing climate.

Factors Favoring the Adoption of Green Finance by Financial Institutions In Dakar: An Exploratory Study

As with any organization, financial institutions, particularly in Dakar, play a crucial role in low-income and emerging economies by mobilizing capital and investing in climate change mitigation. Drawing on neo-institutional theory, this exploratory study aims to understand and identify the factors that may influence the adoption of green finance by banks in Dakar. To achieve this, interviews were conducted with five (5) bank CEOs and three (3) solar energy stakeholders (bank clients), and the data were analyzed using thematic categorical content analysis. The findings, based on the interviewees’ responses, reveal that top management support, proactive institutional strategies, capacity building, incentive policies, market share development, allocation of funds to green projects, and the digitization of banking services are likely to influence the adoption of green finance by banks in Dakar. Furthermore, the direct involvement of monetary and regulatory authorities, through the development of specific regulations that encourage financial institutions to adopt green finance, is essential.

Kolmogorov-Arnold Representation Based Informed Orchestration in Solving Partial Differential Equations for Carbon Balance Sustainability

This paper introduces the Kolmogorov-Arnold Credit Informed Network Orchestration (KACINO), a novel framework proposed to comprehensively structure and analyze the carbon dynamic system. By synthesizing dynamic processes such as carbon emission and sequestration, a credit information of carbon dynamics is built accumulation, KACINO provides a holistic approach to model the complexities inherent in carbon dynamics. Through systematic analysis and scenario simulations, KACINO facilitates informed policy recommendations aimed at optimizing carbon credit utilization and achieving sustainable environmental outcomes. This paper outlines the theoretical foundation, methodology, and practical applications of KACINO, highlighting its potential to support transformative strategies in climate change mitigation and sustainable development.

Impacts of Climate Change: Examining Precipitation, Temperature Anomalies, and Effects on Water Resources in Turkey

Climate change is a significant global issue that affects our everyday lives and water resources. One of the most critical impacts of climate change is on our water resources, which are vital for sustaining life and supporting various ecosystems. Understanding the complex relationship between climate change and water resources is crucial for developing effective mitigation and adaptation strategies. This study aims to investigate the impact of climate change on water resources and to shed light on the importance of addressing this issue. Water is a limited resource, and its availability and quality directly affect human well-being, agriculture, energy production, and ecosystem health. However, climate change alters the patterns and dynamics of precipitation, temperature, and evaporation, leading to significant changes in water availability and distribution. Increasing global temperatures cause glaciers and ice caps to melt, progressively depleting freshwater resources. Moreover, changing precipitation patterns are leading to more frequent and severe droughts in some regions, while others experience increased rainfall and flooding events. The effects of these changes are profound. As water scarcity becomes more widespread, competition for limited resources intensifies, potentially leading to conflicts and social unrest. Given the importance of water resources and the undeniable impact of climate change on their availability and quality, it is imperative that we deepen our understanding of this complex relationship. This study aims to assess the socio-economic and environmental impacts of climate change on water resources. By doing so, it seeks to provide policymakers, scientists, and communities with information that will guide decision-making processes and direct effective climate change adaptation and mitigation strategies. In conclusion, the impact of climate change on water resources cannot be overstated. This study aims to contribute to our understanding of this critical issue by highlighting the urgency of addressing climate change and its consequences on water resources and sustainability. This study examines the effects of changing precipitation patterns and increasing temperatures due to climate change on underground and surface water resources in Turkey. By conducting this research, we hope to pave the way for informed decision-making and encourage collective responsibility to protect our water resources for future generations.

Evaluation the carbon sequestration potentiality of some plant species investigating the highest CO2 consumption for mitigation of climate change

Evaluation the carbon sequestration potentiality of some plant species investigating the highest CO2 consumption for mitigation of climate change

Systematic assessment of the achieved emission reductions of carbon crediting projects

Carbon markets play an important role in firms’ and governments’ climate strategies. Carbon crediting mechanisms allow project developers to earn carbon credits through mitigation projects. Several studies have raised concerns about environmental integrity, though a systematic evaluation is missing. We synthesized studies relying on experimental or rigorous observational methods, covering 14 studies on 2346 carbon mitigation projects and 51 studies investigating similar field interventions implemented without issuing carbon credits. The analysis covers one-fifth of the credit volume issued to date, almost 1 billion tons of CO2e. We estimate that less than 16% of the carbon credits issued to the investigated projects constitute real emission reductions, with 11% for cookstoves, 16% for SF6 destruction, 25% for avoided deforestation, 68% for HFC-23 abatement, and no statistically significant emission reductions from wind power and improved forest management projects. Carbon crediting mechanisms need to be reformed fundamentally to meaningfully contribute to climate change mitigation.

Enhancing high-resolution forest stand mean height mapping in China through an individual tree-based approach with close-range lidar data

Abstract. Forest stand mean height is a critical indicator in forestry, playing a pivotal role in various aspects such as forest inventory, sustainable forest management practices, climate change mitigation strategies, monitoring of forest structure changes, and wildlife habitat assessment. However, there is currently a lack of large-scale, spatially continuous forest stand mean height maps. This is primarily due to the requirement of accurate measurement of individual tree height in each forest plot, a task that cannot effectively be achieved by existing globally covered, discrete footprint-based satellite platforms. To address this gap, this study was conducted using over 1117 km2 of close-range light detection and ranging (lidar) data, which enables the measurement of individual tree heights in forest plots with high precision. Apart from lidar data, this study incorporated spatially continuous climatic, edaphic, topographic, vegetative, and synthetic aperture radar data as explanatory variables to map the tree-based arithmetic mean height (ha) and weighted mean height (hw) at 30 m resolution across China. Due to limitations in obtaining the basal area of individual tree within plots using uncrewed aerial vehicle (UAV) lidar data, this study calculated the weighted mean height through weighting an individual tree height by the square of its height. In addition, to overcome the potential influence of different vegetation divisions at a large spatial scale, we also developed a machine-learning-based mixed-effects (MLME) model to map forest stand mean height across China. The results showed that the average ha and hw across China were 11.3 and 13.3 m with standard deviations of 2.9 and 3.3 m, respectively. The accuracy of mapped products was validated utilizing lidar and field measurement data. The correlation coefficient (r) for ha and hw ranged from 0.603 to 0.906 and 0.634 to 0.889, while the root mean square error (RMSE) ranged from 2.6 to 4.1 and 2.9 to 4.3 m, respectively. Comparing with existing forest canopy height maps derived using the area-based approach, it was found that our products of ha and hw performed better and aligned more closely with the natural definition of tree height. The methods and maps presented in this study provide a solid foundation for estimating carbon storage, monitoring changes in forest structure, managing forest inventory, and assessing wildlife habitat availability. The dataset constructed for this study is publicly available at https://doi.org/10.5281/zenodo.12697784 (Chen et al., 2024).

Expertise and Inequality Amid Environmental Crisis: A View from the Yukon-Kuskokwim Delta

ABSTRACT Scientific expertise is crucial for responding effectively to environmental crises. Nevertheless, under conditions of political inequality, expert policy making can inhibit policy solutions by altering incentives of powerful interest groups. This is the situation facing the predominantly Alaska Native communities of the Yukon-Kuskokwim Delta, which have long relied on salmon for subsistence and are now experiencing a collapse of the salmon population. Scientific evidence indicates that climate change is a primary cause, and experts therefore have opposed demands by Native subsistence fishers for ameliorative measures—especially restricting pollock fishing—as likely to be ineffective. However, this approach eliminates incentives for the influential pollock industry to support policies to address the salmon crisis, including climate-change mitigation. This article presents a simple formal model that demonstrates these incentive effects. This argument contributes to theories of business power and shows how expert policy making can inadvertently force marginalized communities to bear the burden of climate change.

Encouraging water-saving behavior during a "Moment of Change": the efficacy of implementation intentions on water conservation during the transition to university.

Water saving behavior is of substantial importance in climate change mitigation and resilience, including reducing time spent in the shower. However, water use is, for many, a strong habit, and, as such, incorporating new water saving behaviors into one's domestic routines may be unsuccessful. In this study, we consider the extent to which a composite behavior change intervention (of water-saving information, implementation intention formation, and monitoring using a shower timer) is effective in reducing the domestic water consumption of new university students who have recently moved into university accommodation. We focus on aspects of the habit discontinuity hypothesis, namely that a natural moment of change facilitates behavior change by weaking existing habits. The intervention was found to be effective, increasing the frequency of self-reported water-saving behavior over behavior measured in a control group. However, shower times, and water usage (measured at the residential level), were not affected by the intervention, and strength of existing habits, readiness to change water behavior, and recency of starting university were each not significantly associated with the effectiveness of the intervention. However, all participants (irrespective of intervention) increased water-saving behavior and reduced shower time during the study, with residential water usage being less for residences with more participating students. Contrary to expectations, the timing of the intervention did not show a clear effect upon the efficacy of the intervention. We discuss these findings with respect to moments of change and habit discontinuity theory as well as implications for practical behavior change interventions.

Pertubations of Physicochemical Properties and Heavy Metal in the Soil of Coastal Areas of Akwa Ibom State, Nigeria

This study aimed at assessing the concentrations and perturbations of the physicochemical properties and heavy metals in the soil of the coastal regions of Akwa Ibom State, Nigeria. Sampling was carried out across three mangrove locations (Iko Town, Okoroutip and Uta Ewa) in Eastern Obolo, Ibeno and Ikot Abasi Local Government Areas respectively. Four vegetation plots were randomly selected, and within each plot, three belt transects were established. Physicochemical properties [Sand, Clay, Silt, organic carbon (OC), total nitrogen, sulphate and chlorine content] and heavy metals (Arsenic, Silver, Chromium, Copper, Nickel, Lead, Mercury, Cadmium, Titanium, Vanadium and Zinc) were analyzed using standardized methods. Results from this study revealed considerable variations in the physicochemical properties of soil in the study area. The aforementioned heavy metals were present in both wet and dry seasons in considerable concentrations in the soil samples within the study areas. However, were within and below WHO limits. The variations in the physicochemical properties and the presence of these heavy metals in the samples are indicative of an impacted environment unsuitable for the growth and survival of the endemic flora and fauna species of this unique ecosystem. In view of the current knowledge of the role of mangrove ecosystems in blue-economy of the nation and mitigation of ongoing climate change, we advocate a tripartite alliance between the government, multinational companies operating within the area and the various impacted communities that leads to a concerted efforts of constant monitoring and remediation in the reduction of current and future pollutant levels.

Part-Time Farming, Agricultural Socialized Services, and Organic Fertilizer Use: Implications for Climate Change Mitigation

The adoption of organic fertilizers is essential for advancing China’s green agricultural transformation, ensuring food security, and supporting agricultural adaptations. However, several challenges hinder its widespread use in rural areas. This study examines how part-time farming and agricultural service provision influences organic fertilizer use, employing fixed and random effects models on data from 523 households in Shaanxi Province, one of China’s main apple-producing regions. The results reveal: (1) Part-time farming reduces organic fertilizer use by 7.6%, primarily due to labor shortages; (2) Higher non-farm income exacerbates this decline, particularly for Type II part-time farmers; and (3) Mechanized fertilization services help mitigate this reduction. These findings offer valuable policy insights for promoting organic fertilizer adoption in the context of shifting rural labor dynamics and highlight the complex interactions between farming practices and labor migration in the broader trajectory of organic fertilizer use. Moreover, this study highlights the role of organic fertilizer use in enhancing food security while also helping to reduce the carbon footprint of the crop sector in China.

Evaluating Enteric Fermentation-Driven Environmental Kuznets Curve Dynamics: A Bayesian Vector Autoregression Comparative Study of the EU and Least Developed Countries

Global warming and climate change, primarily driven by human activities, with agriculture playing a significant role, have become central topics of scientific research. Livestock production, especially enteric fermentation, is a major source of greenhouse gas emissions, making it a focal point for both climate change adaptation and mitigation strategies. Both the European Union (EU) and Least Developed Countries (LDCs) are highly dependent on agriculture, particularly livestock, which plays a key role in their economic growth. In developing countries, livestock systems are evolving rapidly due to various factors, while in the EU, the livestock sector remains economically and socially significant, representing 36% of total agricultural activity. This study explores the environmental impact of enteric fermentation in livestock production, alongside the economic value it generates in both the EU and LDCs. The analysis utilizes a Bayesian Vector Autoregression (BVAR) methodology, which provides a more robust performance compared to traditional models like Vector Autoregression (VAR) and the Vector-error Correction Model (VECM). This research identifies significant relationships between the variables studied, with structural breaks quantified to reflect the impact of initiatives undertaken in both regions. Interestingly, the results challenge the environmental Kuznets curve, which hypothesizes an inverted U-shaped relationship between economic growth and environmental degradation, as proposed by Stern. This suggests that stronger economic incentives may be necessary to enhance policy effectiveness and promote eco-efficiency. The distinctive characteristics of livestock production in the EU and LDCs should be carefully considered when shaping agricultural policies, with a strong emphasis on farmer education as a critical factor for success. Additionally, corporate management practices must be tailored to address the unique needs, strengths, and challenges of livestock businesses in these two diverse regions.

Analyzing how the timing and magnitude of electricity consumption drive variations in household electricity-associated emissions on a high-VRE grid

Abstract Electrifying the residential sector is critical for national climate change adaptation and mitigation strategies, but increases in electricity demand could drive-up emissions from the power sector. However, the emissions associated with electricity consumption can vary depending on the timing of the demand, especially on grids with high penetrations of variable renewable energy. In this study, we analyze smart meter data from 2019 for over 100 000 homes in Southern California and use hourly average emissions factors from the California Independent System Operator, a high-solar grid, to analyze household CO2 emissions across spatial, temporal, and demographic variables. We calculate two metrics, the annual household electricity-associated emissions (annual-HEE), and the household average emissions factor (HAEF). These metrics help to identify appropriate strategies to reduce electricity-associated emissions (i.e. reducing demand vs leveraging demand-side flexibility) which requires consideration of the magnitude and timing of demand. We also isolate the portion of emissions caused by AC, a flexible load, to illustrate how a load with significant variation between customers results in a large range of emissions outcomes. We then evaluate the distribution of annual-HEE and HAEF across households and census tracts and use a multi-variable regression analysis to identify the characteristics of users and patterns of consumption that cause disproportionate annual-HEE. We find that in 2019 the top 20% of households, ranked by annual-HEE, were responsible for more emissions than the bottom 60%. We also find the most emissions-intense households have an HAEF that is 1.7 times higher than the least emissions-intense households, and that this spread increases for the AC load. In this analysis, we focus on Southern California, a demographically and climatically diverse region, but as smart meter records become more accessible, the methods and frameworks can be applied to other regions and grids to better understand the emissions associated with residential electricity consumption.

Enhancing Alaskan wildfire prediction and carbon flux estimation: a two-stage deep learning approach within a process-based model

Abstract Wildfires in boreal forests release substantial amounts of carbon into the atmosphere. However, current land-surface models are limited in their representation of fire processes, including their ignition and spread. This study thus developed FireDL, a novel data-driven machine-learning model for the prediction of natural wildfires, and combined it with a land-surface model to better understand the impact of fire on carbon fluxes. FireDL has a two-stage deep learning structure that sequentially combines a long short-term memory (LSTM) algorithm and an artificial neural network (ANN). Preliminary random forest analysis identified fire duration as an important factor in predicting the burned area. Thus, in FireDL, the LSTM algorithm was employed to predict fire occurrence and duration, utilizing lightning, vegetation, and climate datasets. Subsequently, the ANN predicted the total burned area using the LTSM-derived fire duration predictions and climate datasets as input. FireDL produced a robust performance in predicting large fires (>10 000 ha), achieving a correlation coefficient of 0.72. The daily-scaled burned area predictions derived from FireDL were integrated into the Community Land Model version 5—Biogeochemistry (CLM5-BGC) to produce CLM5-BGC-FireDL. This integration considerably improved carbon emission estimations. Notably, the total net ecosystem exchange (NEE) estimated using CLM5-BGC-FireDL in 2019, the year with the highest recorded burned area during our study, was twice that estimated using the standard CLM5-BGC. Discrepancies in the NEE can significantly influence atmospheric CO2 levels, highlighting the importance of our fire prediction model in forecasting the burned area and carbon emissions. The use of FireDL with future climate scenarios is thus anticipated to yield valuable insights into ecosystem management and climate change mitigation strategies.

Rice Production and Nitrogen Use Efficiency by Diverse Forms of Fertilization in Rice-Based Crop Rotation Systems

The rising demand for climate change mitigation has brought attention to agricultural systems focused on carbon farming and reducing emissions. Composting food wastes and livestock manure not only mitigates environmental concerns but also boosts soil fertility and crop yields as an alternative fertilizer. In this experiment, we investigated the effects of different fertilizer types (chemical and organic waste compost) and crop rotations (rice–fallow, rice–Italian ryegrass, and rice–potato) on rice production, nitrogen use efficiency, and soil carbon stocks. In this experiment, soil carbon and nitrogen retention were more influenced by compost nutrient levels than by crop rotation types. Overall, as the nitrogen levels increased, the rice yields improved with both chemical and organic waste fertilizers. Among the crop rotations, the rice–Italian ryegrass rotation showed a higher nitrogen use efficiency. Optimal fertility levels, balancing nitrogen use efficiency, yield, and soil carbon were observed between 523 and 582 kg N ha−1 when combined with specific crop rotations. Moreover, soil total carbon and soil total nitrogen varied among crop rotation systems. Our results indicate that organic waste compost can be a potential alternative to chemical fertilizers, while crop rotations offer a viable approach for maximizing the environmental benefits.

Land-use competition in 1.5°C climate stabilization: is there enough land for all potential needs?

Achieving a low-carbon future requires a comprehensive approach that combines emission mitigation options from economic activities with the sustainable use of land for numerous needs: food production, energy production, carbon sequestration, nature preservation and broad ecosystem services. Using the MIT Integrated Global System (IGSM) framework we analyze land-use competition in a 1.5°C climate stabilization scenario, in which demand for bioenergy and natural sinks increase along with the need for sustainable farming and food production. We find that to address the numerous trade-offs, effective approaches to nature-based solutions (NBS) and agriculture practices are essential. With proper regulatory policies and radical changes in current practices, global land is sufficient to provide increased consumption of food per capita (without large diet changes) over the century while also utilizing 2.5–3.5 billion hectares (Gha) of land for NBS practices that provide a carbon sink of 3–6 gigatonnes (Gt) of CO2 per year as well as 0.4–0.6 Gha of land for energy production—0.2–0.3 Gha for 50–65 exajoules (EJ) per year of bioenergy and 0.2–0.35 Gha for 300–600 EJ/year of wind and solar power generation. We list the competing uses of land to reflect the trade-offs involved in land use decisions, and note that while there is sufficient land in our scenario, attaining this outcome, capable of delivering a 1.5°C future, requires effective policies and measures at national and global levels that promote efficient land use for food, energy and nature (including carbon sequestration) and ensure long-term commitments by decision makers from governments and industry in order to realize the benefits of climate change mitigation.

More-than-Human Net Zero Futures

From industry through policymaking to academia, much prevailing sustainability discourse focusses on transitioning to a so-called ‘Net Zero future’. Central to this vision is mitigation of human-driven climate change through the decarbonisation of industrial society, principally via increased innovation and adoption of emergent technologies. This paper argues Design Research must pivot from these reductive, solutionist narratives, and develop a disruptive yet inclusive approach towards designing for Net Zero. In response to the complexity of climate change, this paper proposes a novel conceptual frame that helps designer-practitioners to challenge the unsustainable technocentric status quo. Built upon a confluence of Speculative, Participatory and More-than-Human-Centred methods, the paper outlines how this approach can stimulate close collaboration between designers and stakeholder networks. The paper asserts that, through this scaffold, designer-practitioners can reimagine responsible technological Net Zero futures which are inherently More-than-Human, that is, sustainable and equitable for our planet’s human and non-human stakeholders alike.

Econometrics of Climate Change Research

The econometrics of climate change explores the intricate interplay between economic activity and climate dynamics, offering empirical insights into the causes, impacts, and mitigation of global climate change. This research applies advanced econometric techniques to quantify the economic consequences of environmental changes and evaluate the effectiveness of climate policies. By leveraging panel data models, time-series analysis, and causal inference methods, this study investigates critical topics such as the economic costs of extreme weather events, the efficacy of carbon pricing mechanisms, and the role of renewable energy adoption in mitigating climate risks. Additionally, this research incorporates machine learning techniques to enhance predictive accuracy in modeling future climate-economic interactions. Through rigorous empirical analysis, the study provides actionable recommendations for policymakers to design sustainable strategies that balance economic growth with climate resilience. The findings contribute to a deeper understanding of the socioeconomic dimensions of climate change, emphasizing the importance of evidence-based decision-making in addressing one of humanity’s most pressing challenges.