Search Results (298)

Ecosystem services—the benefits humans derive from nature—are foundational to environmental sustainability and economic well-being, with carbon sequestration and storage standing out as critical regulating services in the fight against climate change. This study presents a comprehensive financial valuation of the carbon sequestration, storage and product substitution ecosystem services provided by the Hungarian forest-based sector. Using a multi-scenario framework, four complementary valuation concepts are assessed: total carbon storage (biomass, soil, and harvested wood products), annual net sequestration, emissions avoided through material and energy substitution, and marketable carbon value under voluntary carbon market (VCM) and EU Carbon Removal Certification Framework (CRCF) mechanisms. Data sources include the National Forestry Database, the Hungarian Greenhouse Gas Inventory, and national estimates on substitution effects and soil carbon stocks. The total carbon stock of Hungarian forests is estimated at 1289 million tons of CO₂ eq, corresponding to a theoretical climate liability value of over EUR 64 billion. Annual sequestration is valued at approximately 380 million EUR/year, while avoided emissions contribute an additional 453 million EUR/year in mitigation benefits. A comparative analysis of two mutually exclusive crediting strategies—improved forest management projects (IFMs) avoiding final harvesting versus long-term carbon storage through the use of harvested wood products—reveals that intensified harvesting for durable wood use offers higher revenue potential (up to 90 million EUR/year) than non-harvesting IFM scenarios. These findings highlight the dual role of forests as both carbon sinks and sources of climate-smart materials and call for policy frameworks that integrate substitution benefits and long-term storage opportunities in support of effective climate and bioeconomy strategies. Full article

Developing accurate activity data on tree biomass using remote sensing tools such as LiDAR and drone-mounted sensors is essential for improving carbon accounting in the agricultural sector. However, direct biomass measurements of perennial fruit trees remain limited, especially for validating remote sensing estimates. This study evaluates the potential of terrestrial laser scanning (TLS) and drone-mounted RGB sensors (Drone_RGB) for estimating biomass in two major perennial crops in South Korea: apple (‘Fuji’/M.9) and citrus (‘Miyagawa-wase’). Trees of different ages were destructively sampled for biomass measurement, while volume, height, and crown area data were collected via TLS and Drone_RGB. Regression analyses were performed, and the model accuracy was assessed using R², RMSE, and bias. The TLS-derived volume showed strong predictive power for biomass (R² = 0.704 for apple, 0.865 for citrus), while the crown area obtained using both sensors showed poor fit (R² ≤ 0.7). Aboveground biomass was reasonably estimated (R² = 0.725–0.865), but belowground biomass showed very low predictability (R² < 0.02). Although limited in scale, this study provides empirical evidence to support the development of remote sensing-based biomass estimation methods and may contribute to improving national greenhouse gas inventories by refining emission/removal factors for perennial fruit crops. Full article

Personal lifestyle choices contribute up to 75% of national emissions and yet the greenhouse gas (GHG) inventories included in the National Inventory Report (NIR) of Canada provide limited insight on these choices. Better insight can be found using carbon footprint calculators that estimate individual emissions; however, they vary in regard to their input parameters, output data, and calculation methods. This study assessed five calculators, which are popular with the public, or compatibility with the Canadian NIR. A quantitative scoring matrix was developed to assess the output depth, academic proficiency, and effectiveness of the calculators to inform lifestyle changes, alongside NIR alignment. The results showed that the calculator with the overall highest cumulative score across all the comparative criteria was the one offered by Carbon Footprint Ltd. The other calculators that scored highly include CoolClimate Calculator and Carbon Independent. The potential of the calculators in regard to informing low-carbon lifestyles can be improved through the incorporation of more depth in terms of capturing the purchase information of goods and services and providing detailed secondary information to users, including mitigation strategies and carbon offset options. The main driver of incompatibility between the calculator tools and the NIR was the different approaches taken to the emissions inventory, with the NIR using a territorial framework and the calculators being consumption driven. The outcomes of this study demonstrate a global need for the evolution of NIR structuring to increase its relatability with citizens and for the improved standardization of publicly available tools. Full article

Agricultural greenhouse gas (GHG) emissions contribute significantly to climate change and underline the importance of reliable measurements and mitigation strategies. This life cycle assessment (LCA)-based study evaluates the environmental impacts of four key Mediterranean agricultural products, namely olives, sweet potatoes, corn, and grapes using GHG measurements at four pilot fields located in different regions of Greece. With the use of a cradle-to-gate approach six environmental impact categories, more specifically acidification potential (AP), eutrophication potential (EP), global warming potential (GWP), ozone depletion potential (ODP), photochemical ozone creation potential (POCP), and cumulative energy demand (CED) as energy-based indicator are assessed. The functional unit used is 1 ha of cultivated land. Any potential carbon offsets from mitigation practices are assessed through an integrated low-carbon certification framework and the use of innovative, site-specific technologies. In this context, the present study evaluates three life cycle inventory (LCI)-based scenarios: Baseline (BS), which represents a 3-year crop production period; Field-based (FS), which includes on-site CO₂ and CH₄ measurements to assess the effects of mitigation practices; and Inventoried (IS), which relies on comprehensive datasets. The adoption of carbon mitigation practices under the FS scenario resulted in considerable reductions in environmental impacts for all pilot fields assessed, with average improvements of 8% for olive, 5.7% for sweet potato, 4.5% for corn, and 6.5% for grape production compared to the BS scenario. The uncertainty analysis indicates that among the LCI-based scenarios evaluated, the IS scenario exhibits the lowest variability, with coefficient of variation (CV) values ranging from 0.5% to 7.3%. In contrast, the FS scenario shows slightly higher uncertainty, with CVs reaching up to 15.7% for AP and 14.7% for EP impact categories in corn production. The incorporation of on-site GHG measurements improves the precision of environmental performance and supports the development of site-specific LCI data. This benchmark study has a noticeable transferability potential and contributes to the adoption of sustainable practices in other regions with similar characteristics. Full article

Land managers across the United States (U.S.) are developing plans to mitigate climate change. Effective implementation and monitoring of these climate action plans require standardized methods and timely, accurate geospatial data at appropriate resolutions. Despite the abundance of geospatial and statistical data in the U.S., a significant gap remains in translating these data into actionable insights. To address this gap, we developed the Land Emissions and Removals Navigator (LEARN), an online tool that automates subnational greenhouse gas (GHG) inventories of forests and trees in nonforest lands using a standardized analytical framework consistent with national and international guidelines. LEARN integrates multiple datasets to calculate land cover and tree canopy changes, delineate areas of forest disturbance, and estimate carbon emissions and removals. To demonstrate the application of LEARN, this paper presents case studies in Jefferson County, Washington; Montgomery County, Maryland; and federally owned forests across the conterminous U.S. Our results highlight LEARN’s capacity to provide localized insights into carbon dynamics, enabling subnational entities to develop tailored climate strategies. By enhancing accessibility to standardized data, LEARN empowers community land managers to more effectively mitigate climate change. Future developments aim to expand LEARN’s scope to cover nonforest landscapes and incorporate additional decision-making functionalities. Full article

As road traffic in Turkey is a significant source of emissions due to the increasing number of vehicles on the road, the goal of this study is to calculate greenhouse gas emissions from Turkey’s roads between 2010 and 2020, create an inventory, and estimate possible emissions until 2050. In the study, both greenhouse gases (carbon dioxide (CO₂) and nitrous oxide (N₂O) and co-emitting air pollutants that indirectly contribute to climate change (ammonia—NH₃, nitrogen oxide—NO_X, sulfur dioxide—SO₂, carbon monoxide—CO, non-methane volatile organic compounds—NMVOC, and particulate matter—PM) were investigated. The study revealed that the total number of vehicles using state roads in Turkey increased by 60% between 2010 and 2020. As a result, emissions of CO₂, N₂O, NH₃, NO_X, SO₂, CO, NMVOC, and PM increased by 29.6%, 24.2%, 0.5%, 19.9%, 9.9%, 18.2%, 21.5%, and 39.7%, respectively. When emissions were analyzed on a provincial basis, particular attention was drawn to provinces with high levels of urbanization. Based on forecast studies, the total number of vehicles registered for traffic will increase by 105% by 2050. Due to this increase, CO₂, N₂O, NH₃, NO_X, SO₂, CO, NMVOC, and PM emissions are estimated to increase by 149.17%, 151.78%, 154.39%, 138.95%, 150.97%, 153.09%, 152.09%, and 151.47%, respectively. Full article

The Amazonian forests located within the Guiana Shield store above-average levels of biomass per hectare. However, considerable uncertainty remains regarding carbon stocks in this region, mainly due to limited inventory data and the lack of spatial datasets that account for factors influencing variation among forest types. The present study investigates the spatial distribution of original total forest biomass in the state of Amapá, located in the northeastern Brazilian Amazon. Using data from forest inventory plots, we applied geostatistical interpolation techniques (kriging) combined with environmental variables to generate a high-resolution map of forest biomass distribution. The stocks of biomass were associated with different forest types and land uses. The average biomass was 536.5 ± 64.3 Mg ha⁻¹ across forest types, and non-flooding lowland forest had the highest average (619.1 ± 38.3), followed by the submontane (521.8 ± 49.8) and the floodplain (447.6 ± 45.5) forests. Protected areas represented 84.1% of Amapá’s total biomass stock, while 15.9% was in agriculture and ranching areas, but the average biomass is similar between land-use types. Sustainable-use reserves stock more biomass (40%) than integral-protection reserves (35%) due to the higher average biomass associated with well-structured forests and a greater density of large trees. The map generated in the present study contributes to a better understanding of carbon balance across multiple spatial scales and demonstrates that forests in this region contain the highest carbon stocks per hectare (260.2 ± 31.2 Mg ha⁻¹, assuming that 48.5% of biomass is carbon) in the Amazon. To conserve these stocks, it is necessary to go further than merely maintaining protected areas by strengthening the protection of reserves, restricting logging activities in sustainable-use areas, promoting strong enforcement against illegal deforestation, and supporting the implementation of REDD+ projects. These actions are critical for avoiding substantial carbon stock losses and for reducing greenhouse-gas emissions from this region. Full article

Climate change has driven global awareness of environmental issues, leading to the adoption of clean technologies aimed at reducing Greenhouse Gas (GHG) emissions. An effective method to assess environmental mitigation is the quantification of the Product Carbon Footprint (PCF) in the Life Cycle Assessment (LCA) of production processes. In the oil extraction industry, artificial lift systems use electro submersible pumps (ESPs) that can now incorporate new operating principles based on permanent magnet motors (PMMs) and CanSystem (CS) as an alternative to traditional normal induction motors (NIMs) and can help lower the carbon footprint. This study compares the PCF of ESPs equipped with PMMs and CS versus NIMs, using LCA methodologies in accordance with ISO 14067:2018 for defining the Functional Unit (FU) and ISO 14064-1:2019 to calculate the GHG inventory and the amount of CO₂ equivalent per year. The analysis spans five key stages and 14 related activities. For ESPs with NIMs, this study calculated 999.9 kg of raw materials, 1491.66 kW/h for manufacturing and storage, and 5.77 × 10⁴ kW/h for use. In contrast, ESPs with PMMs and CS required 656 kg of raw materials and consumed 4.44 × 10⁴ kW/h during use, resulting in an 23% reduction in energy consumption. This contributed to an 21.9% decrease in the PCF. The findings suggest that PMMs and CS offer a sustainable solution for reducing GHG emissions in oil extraction processes globally. Full article

The global shipping industry facilitates the movement of approximately 80% of goods across the world but accounts for nearly 3% of total greenhouse gas (GHG) emissions every year, and other pollutants. One challenge in reducing shipping emissions is understanding and quantifying emission characteristics. A detailed method for calculating shipping emissions should be applied when preparing exhaust gas inventory. This research focused on quantifying CO₂, NO_x, and SO_x emissions from tankers, containers, bulk carriers, and general cargo in the Republic of Korea using spatio-temporal analysis and maritime big data. Using the bottom-up approach, this study calculates vessel emissions from the ship engines while considering the fuel type and operation mode. It leveraged the Geographic Information System (GIS) to generate spatial distribution maps of vessel exhausts. The research revealed variability in emissions according to ship types, sizes, and operational modes. CO₂ emissions were dominant, totaling 10.5 million tons, NO_x 179,355.2 tons, and SO_x 32,505.1 tons. Tankers accounted for about 43.3%, containers 33.1%, bulk carriers 17.3%, and general cargo 6.3%. Further, emissions in hoteling and cruising were more significant than during maneuvering and reduced speed zones (RSZs). This study contributes to emission databases, providing a basis for the establishment of targeted emission control policies. Full article

Freight mode choice and the resulting inventory implications significantly influence a product’s carbon footprint. This paper investigates mode selection under a voluntary carbon emissions constraint. Slower modes such as inland waterways and ocean freight are less expensive and emit less greenhouse gas (GHG), but they require higher inventory levels due to longer lead times. In contrast, faster modes like less-than-truckload (LTL) shipping reduce inventory needs but incur higher transportation costs and emissions. Mode choice thus involves trade-offs between transport cost, inventory holding, lead time uncertainty, and GHG emissions from transportation and warehousing. This paper develops a comprehensive inventory-transportation model under the stochastic demand and lead time to evaluate these trade-offs and guide sustainable freight decisions. The model is a practical toolbox that enables managers to evaluate how freight mode choice and inventory policy affect costs and emissions under different operational scenarios and carbon constraints. Full article

This entry describes the methods used to quantify methane emissions from either point or area sources using downwind methods. The methods described could be used as a practical guide to quantify emissions of any trace gas type from either a point or area emission source. Methane is a relatively strong greenhouse gas, its GWP is 25 times larger than CO₂ over a 100-year period, and an increase in methane anthropogenic emissions has been correlated to a changing global climate. Emission estimates that are calculated and used for national inventories are usually derived from bottom-up approaches, however there is now an increasing pressure for these to be validated by direct measurement. Calculating emission rates from downwind measurements has proven to be a versatile and relatively simple approach for direct measurement. Downwind measurement method descriptions are presented here as a practicable guide to quantifying point and area source emissions. Emission quantification is a two-stage process where methane concentration and meteorological data must be measured downwind of a source and then converted to emissions using an atmospheric dispersion model. Only four technology types currently measure in the range typical of downwind methane concentrations: metal oxide sensors, non-dispersive infrared sensors, tunable diode laser absorption spectrometers and optical cavity instruments. The choice of methane measurement is typically determined by the size of the emission source, location and the budget of the project. Meteorological data are essential to quantifying emissions, especially regarding wind speed and direction. In most cases, simple atmospheric dispersion approaches can be used to quantify both area and point emissions using these downwind measurements. Emissions can be generated using limited data (only methane concentration, wind speed, wind direction, and locations are necessary), but quantification uncertainty can be reduced using more input data. Site selection and location of instrument deployment are essential because quantification approaches assume a flat fetch (no aerodynamic obstructions) and constant wind fields. When modeling assumptions are violated, quantification uncertainty can range between +250% and −100% of the actual emission rate. At present there, is no happy medium between modeling complexity and computational time, and this remains the biggest challenge for downwind emission quantification. Full article

The accelerated development of construction has positively boosted the economy but caused environmental pollution. The objective of this article was to propose a model to quantify and identify the causes of environmental aspects in urban infrastructure projects during the pre-construction phase. The methodology included ten environmental aspects and six construction activities, distributed in three phases: quantification, preparation of an inventory and identification of the main causes of environmental aspects. The results of projects A, B and C confirmed five environmental aspects with quantifications of the maximum normalized unitary ratios, namely greenhouse gas emissions with 1.00, 1.00 and 1.00; energy use with 0.99, 0.99 and 1.00; noise pollution with 0.84, 0.89 and 1.00; water use with 0.95, 1.00 and 0.96 and use of resources and raw materials with 1.00, 1.00 and 1.00, caused by the construction methods, machines, materials and environmental conditions of the urban infrastructure project. This proposal was the first initiative to analytically and previously investigate the environmental performance of future infrastructure works, assisting in the strategies of the pre-construction phase and improving the decision-making process regarding environmental issues in constructions. Finally, this initial effort may evolve to create opportunities to avoid, mitigate, reduce or accept the environmental effects in the infrastructure projects in developing countries. Full article

Soybean is a major vegetable protein crop often considered to be a sustainable alternative to animal products. Assessments of soybean sustainability often resort to Life Cycle Assessments (LCAs), which are difficult to compare due to methodological inconsistencies. This study carried out an innovative method for harmonized comparisons of soybean production between farms assessed in different studies. Rather than collecting LCA results, we collected Life Cycle Inventories (LCIs) and then calculated the global warming potential (GWP) and land use impacts of each farm. For this, we carried out a systematic review following the PRISMA methodology to collect LCI data from 19 studies representing 126 farms in six countries. A comparable analysis of the farms showed a higher variability in GWP (0.27–1.53 kg CO₂e/kg of soybean) than previous reviews, but within a range similar to the results of original studies. As the same LCA method and data were used for all cases, this range can be explained by differences between production systems and locations, with a minimum contribution from methodological variability. Farms in Iran and the United States exhibited the highest emissions, primarily driven by synthetic fertilizer use, irrigation, and energy use. Using results from original studies, farms in Iran showed a substantially lower GWP. Farms in Brazil showed lower non-biogenic greenhouse gas emissions but the highest soil biotic capacity loss due to land occupation, while Italian farms demonstrated minimal land use impacts. These findings underscore the need for region-specific mitigation strategies, despite being limited by data gaps on residue management, the global representativity of the sample of farms, and a lack of detail in fertilizer and irrigation data. There is a pressing need for more complete reporting of LCA study results. Full article

This study assesses the sustainability of bioethanol production from multiple agricultural feedstocks, including corn stover, wheat straw, and rice husk, using a life cycle assessment (LCA) method. The process focuses on converting lignocellulose biomass into bioethanol through advanced biotechnology, enriching energy security and supporting sustainable development in Pakistan. The process includes various stages of feedstock utilization, including cultivation, harvesting, transportation, preprocessing, and conversion, eventually yielding 1 kg of bioethanol with different inventories for each of the three feedstocks. A comparative analysis of the three feedstocks reveals that the wheat straw showed the highest environmental impacts, while rice husk exhibits the least environmental impacts and emerges as a more sustainable and viable option for bioethanol production. The economic assessment revealed the feasibility of bioethanol production, achieving a daily revenue of $9600 and a monthly income of $211,200, based on 22 working days in a single 8 h shift. The total initial capital investment cost was estimated at $478,515, while operational costs were calculated at $225,921. The external cost of the plant was evaluated at $14.23. Transitioning from grid-mix to renewable energy, such as photovoltaic systems, showed a reduction among three feedstocks. Therefore, bioethanol production not only addresses waste management challenges but also contributes to waste-to-energy conversion and renewable energy generation, aligning with public health goals and sustainable development. The findings highlight the potential of bioethanol production as a strategic solution to manage agricultural waste sustainably and reduce greenhouse gas emissions. Full article

The construction sector is a significant contributor to global greenhouse gas (GHG) emissions, necessitating urgent decarbonization efforts to align with international climate goals such as the Paris Agreement and the European Green Deal. This study explores a comprehensive framework for construction companies to map and reduce their GHG emissions through a structured four-step approach: defining emission scopes, conducting GHG inventories, setting reduction targets, and planning actionable reductions. Four key pathways are proposed: electricity decarbonization through renewable energy adoption and energy efficiency measures; direct emissions reduction via fleet electrification and infrastructure optimization; recycling and resource efficiency improvements through waste diversion and material reuse; and supply chain emissions reduction by enforcing sustainability standards and responsible sourcing practices. The analysis highlights the importance of integrating technological, organizational, and policy-driven solutions, such as rooftop photovoltaic systems, virtual power purchase agreements, waste management strategies, and supplier codes of conduct aligned with global sustainability benchmarks. The study concludes that construction companies can achieve significant emission reductions by adopting a structured, multi-pathway approach; emphasizing progress over perfection; and aligning their strategies with national and international climate targets. This research provides actionable insights for the construction sector to transition toward a net-zero future by 2050. Full article