Amount Of Greenhouse Gas Emissions Research Articles

The Hidden Carbon Cost of Forest Fire Management: Quantifying Greenhouse Gas Emissions from Both Vegetation Burning and Social Rescue Activities in Yajiang County, China

Quantifying greenhouse gas (GHG) emissions from forest fires is essential for climate change mitigation strategies, yet current methodologies predominantly focus on vegetation combustion, neglecting emissions from firefighting operations. This study presents a comprehensive assessment of GHG emissions from a forest fire in Yajiang County, China, by integrating remote sensing data with ground-based measurements to quantify emissions from both vegetation combustion and emergency response activities. Analysis revealed that the fire, which affected 20,688.67 hectares, generated 63,764.59 tons of GHGs—with vegetation combustion accounting for 83.5% (53,266.29 tons) and emergency response activities contributing 16.5% (10,498.30 tons). Moderate-severity fires in evergreen forests yielded the highest emissions, while aerial operations constituted the primary source of emergency-response-related emissions. Significantly, NOx emissions from emergency response activities exceeded those from vegetation combustion. This research advances forest fire management by establishing a holistic accounting framework that incorporates previously unquantified emission sources, thereby providing foundational data for developing environmentally optimized fire social rescue activity protocols.

Flexible Carbon Source Regulation for Mitigating Greenhouse Gas Emissions in Full-Scale Wastewater Treatment.

Achieving carbon neutrality in wastewater treatment plants (WWTPs) by 2060 requires effective strategies to mitigate greenhouse gas (GHG) emissions. This study explores the potential of flexible carbon source regulation to reduce GHG emissions while improving the nutrient removal efficiency under varying influent conditions. A plant-wide model was developed, calibrated with one year of hourly monitoring data, to quantify GHG emissions in a full-scale WWTP. We evaluated three carbon regulation strategies: anaerobic digestion (AD), short fermentation (SF), and adaptive carbon source regulation (ACSR). Results show that AD was most effective in high carbon-to-nitrogen (C/N) ratios, reducing GHG emissions by 29.2%. SF performed best at low C/N ratios, reducing N2O emissions by 56.3%. The ACSR strategy, which dynamically adjusts between AD and SF based on influent C/N, achieved a total GHG reduction of 18.2% compared with the base strategy (conventional activated sludge process with mechanical dewatering and landfill disposal of waste sludge). Seasonal variations significantly impacted emissions (p < 0.001), while C/N ratios, although not statistically significant in isolation, play a crucial role in influencing N2O emissions and sludge production, emphasizing the need for adaptive carbon allocation. This study underscores the viability of plant-wide, data-driven carbon source regulation as an integrated approach to achieving net-zero emissions in WWTPs, effectively addressing diverse GHG emission sources within the system.

PATHWAY TO NET ZERO 2050: A CONCEPTUAL EVIDENCE FOR SMES TRANSITIONING TO SUSTAINABLE DEVELOPMENT

This paper offers a conceptual understanding of the context under which SMEs attempt to shift towards achieving Net Zero emissions by the year 2050. The study examines the progression of sustainable development goals and the significant contribution of SMEs in attaining these objectives by including theoretical and conceptual understandings of SMEs. The Brundtland Report and the Rio Earth Summit originated the sustainable development agenda. These events paved the way for establishing the Sustainable Development Goals (SDGs) 2015. Attaining Net Zero 2050, a condition where the amount of greenhouse gas emissions is offset by removing them from the atmosphere, is crucial for worldwide efforts to address climate change by Sustainable Development Goal 13. SMEs have distinct obstacles and prospects throughout this transformation, encompassing limitations in financial resources, technological preparedness, and compliance requirements. However, they also have the potential to gain advantages such as enhanced efficiency, innovation, and market competitiveness. The paper employs the Technology-Organization-Environment (TOE) framework and the Resource-Based View (RBV) paradigm to examine SMEs' successful implementation of sustainable practices. In addition, this analysis explores the frequently disregarded impact of digital transformation on improving the sustainability efforts of SMEs. The investigation asserts that a well-coordinated strategy is essential for SMEs to effectively achieve the goal of Net Zero 2050 and contribute to global sustainability objectives. This strategy should involve government incentives, industry support, and internal commitment within the organizations. By adopting this approach, SMEs can ensure long-term economic and environmental resilience.

Locating and quantifying CH4 sources within a wastewater treatment plant based on mobile measurements

Abstract. Wastewater treatment plants (WWTPs) are substantial contributors of greenhouse gas (GHG) emissions because of the high production of methane (CH4) and nitrous oxide (N2O). A typical WWTP complex contains multiple functional areas that are potential sources of GHG emissions. Accurately quantifying GHG emissions from these sources is challenging due to the inaccuracy of activity data, the ambiguity of emission sources, and the absence of monitoring standards. Locating and quantifying WWTP emission sources using a measurement-based GHG emission quantification method is crucial for evaluating and improving traditional emission inventories. In this study, CH4 mobile measurements were conducted within a WWTP complex in the summer and winter of 2023. We utilized a multi-source Gaussian plume model combined with a genetic algorithm inversion framework, designed to locate major sources within the plant and quantify the corresponding CH4 emission fluxes. We identified 13 main sources in the plant and estimated plant-scale CH4 emission fluxes of 68.78±17.40kgh-1 (603.33±152.66ta-1) for the summer and 47.76±21.39kgh-1 (418.95±187.59ta-1) for the winter. The predominant sources of CH4 emissions were the screen and primary clarifier, contributing 55 % and 67 % to the total emissions in summer and winter, respectively. The comparison revealed that summer CH4 emissions were 2.8 times higher than inventory estimates, while winter emissions were twice the inventory values. This study demonstrated that mobile measurements, combined with the multi-source Gaussian plume inversion framework, are a powerful tool to locate and quantify GHG sources at a complex site, with the potential for further refinement to accommodate different types of factories and gas species.

Sustainable Protein Alternatives: Exploring Plant-Based, Microbial and Novel Protein Sources

Population growth, changes in diets and sustainability issues support the global demand for protein. Animal-based protein sources are responsible for substantial amounts of greenhouse gas emissions, deforestation and water wastage, urging the assessment of sustainable alternatives. Plant proteins, extracted from soy, peas, lentils and other legumes, have a smaller carbon footprint and health advantages less saturated fat and cholesterol. Laboratories are creating proteins from microbes, algae and insects and lab-grown meat, with all sorts of pros and cons about each medication. Screens have been developed for alternative proteins like corn starch, wheat starch, pea starch, potato starch, etc. Machines such as high moisture extruders, fermenters and extractors have developed and improved their capabilities, which has decreased their cost over time. The system cost is increased, but the product quality, functionality and palatability have improved, making it comparable to animal-based sources. But challenges remain in the form of consumer acceptance, expensive manufacturing processes and regulatory complexities that continue to slow their mass-production implementation. Combating these challenges may take multiple forms, from researching better protein in terms of quality and digestibility, to policy changes that promote new protein marketplaces, to consumer education around environmentally and health beneficial protein selections. To fulfil the nutrition needs of a global population in an ecologically-friendly manner moving forward, the food systems will need to include plant-based and novel sources of protein. For those who hear of alternative proteins and think it is a far-off trend over the horizon, an insufficient presence to solve large-scale hunger and climate challenges, think again. The current study examines the options, advantages and drawbacks of alternative proteins, a field that requires ongoing investigation and policy framing to help define the course of the global nutrition narrative.

The knowns and unknowns in our understanding of how plastics impact climate change: a systematic review

Plastic pollution and climate change are two major environmental issues of this century, with implications for ecosystem health, the economy, and humankind. Plastics have the potential to affect the climate in multiple ways, yet we lack a thorough understanding of what data we have on this phenomenon and where the knowledge gaps are. Here, we conducted a systematic review to assemble knowledge and answer the question: How do plastics impact climate through three major mechanisms–emissions of greenhouse gases across the plastics lifecycle, interference with Earth’s carbon sinks, and interference with Earth’s radiation budget? We searched through all 14 databases in Web of Science for relevant articles, and amended this pool with articles from manual reference searching and expert elicitations. Using rigorous inclusion and exclusion criteria, including the exclusion of non-peer reviewed studies to minimize risk of bias, we ultimately selected 143 articles for our review - 36 lifecycle greenhouse gas emissions papers, 83 carbon sink papers, and 24 radiation budget papers. Based on current available data, we found that the plastics lifecycle can emit up to two gigatonnes of carbon dioxide equivalents per year, with the most emissions being produced at the primary production and product manufacture stages. From existing carbon sink studies, we identified more instances of plastics negatively affecting carbon sequestration than vice versa. From the radiation budget papers, we found that radiative impacts are predominantly cooling in nature. The body of evidence is incomplete and more research is needed to confirm these findings and fill in existing knowledge gaps. Future work should prioritize quantifying greenhouse gas emissions from the transportation, consumption, and unmanaged waste stages of the plastics lifecycle. We need more studies that examine the impact of plastics on coastal blue carbon ecosystems and marine carbon sequestration endpoints, and more studies examining the impact of plastics on direct radiative forcing via aerosols, cloud properties, and the albedo/melting rate of surfaces and ice/snow. Immediate action is required to decarbonize the plastics lifecycle, and full accounting of the climate impact of plastics is needed in emissions scenarios, inventories, and climate models across geographies and sectors.

Quantification of greenhouse gas emissions from a municipal solid waste incinerator using an uncrewed aerial vehicle.

Quantification of greenhouse gas emissions from a municipal solid waste incinerator using an uncrewed aerial vehicle.

The North American Greenhouse Gas Budget: Emissions, Removals, and Integration for CO2, CH4, and N2O (2010–2019): Results From the Second REgional Carbon Cycle Assessment and Processes Study (RECCAP2)

AbstractAccurate accounting of greenhouse‐gas (GHG) emissions and removals is central to tracking progress toward climate mitigation and for monitoring potential climate‐change feedbacks. GHG budgeting and reporting can follow either the Intergovernmental Panel on Climate Change methodologies for National Greenhouse Gas Inventory (NGHGI) reporting or use atmospheric‐based “top‐down” (TD) inversions or process‐based “bottom‐up” (BU) approaches. To help understand and reconcile these approaches, the Second REgional Carbon Cycle Assessment and Processes study (RECCAP2) was established to quantify GHG emissions and removals for carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O), for ten‐land and five‐ocean regions for 2010–2019. Here, we present the results for the North American land region (Canada, the United States, Mexico, Central America and the Caribbean). For 2010–2019, the NGHGI reported total net‐GHG emissions of 7,270 TgCO2‐eq yr−1 compared to TD estimates of 6,132 ± 1,846 TgCO2‐eq yr−1 and BU estimates of 9,060 ± 898 TgCO2‐eq yr−1. Reconciling differences between the NGHGI, TD and BU approaches depended on (a) accounting for lateral fluxes of CO2 along the land‐ocean‐aquatic continuum (LOAC) and trade, (b) correcting land‐use CO2 emissions for the loss‐of‐additional‐sink capacity (LASC), (c) avoiding double counting of inland water CH4 emissions, and (d) adjusting area estimates to match the NGHGI definition of the managed‐land proxy. Uncertainties remain from inland‐water CO2 evasion, the conversion of nitrogen fertilizers to N2O, and from less‐frequent NGHGI reporting from non‐Annex‐1 countries. The RECCAP2 framework plays a key role in reconciling independent GHG‐reporting methodologies to support policy commitments while providing insights into biogeochemical processes and responses to climate change.

Associations Between Inflammatory Potential of Diet with the Risk of All-Cause Mortality and Greenhouse Gas Emissions in Chinese Adults.

Background: Current research inadequately substantiates the impacts of dietary inflammatory potential based on the dietary inflammatory index (DII) on population health and environmental sustainability in Chinese adults. Objectives: We aimed to investigate the associations between the DII with the risk of all-cause mortality and greenhouse gas (GHG) emissions in Chinese adults. Methods: Data from adults (N = 15,318) in the China Health and Nutrition Survey 1997-2015 wave were included in the analysis. DII and energy-adjusted DII (E-DII) were calculated using dietary intake data collected with a combination of 3-day consecutive 24-h dietary recalls and the food weighing method. The total GHG emissions were calculated by summing the amount of emissions from all the food groups consumed by the participants. Cox proportional hazards regression models and linear regression models were conducted for statistical analysis. Results: A pro-inflammatory diet, as reflected by higher DII and E-DII scores was associated with an increased risk of all-cause mortality (DII: Q5 vs. Q1: HR = 1.82; 95% CI: 1.45-2.30; p-trend < 0.0001; E-DII: Q5 vs. Q1: HR = 1.86; 95% CI: 1.38-2.52; p-trend < 0.0001) and higher amounts of GHG emissions (both p-trend < 0.0001). Conclusions: These findings demonstrated positive associations between pro-inflammatory potentials with an increased risk of all-cause mortality and higher GHG emissions among Chinese adults, suggesting dual adverse impacts of a pro-inflammatory diet on health and environmental sustainability.

Emission Factor Recommendation for Life Cycle Assessments with Generative AI.

Accurately quantifying greenhouse gas (GHG) emissions is crucial for organizations to measure and mitigate their environmental impact. Life cycle assessment (LCA) estimates the environmental impacts throughout a product's entire lifecycle, from raw material extraction to end-of-life. Measuring the emissions outside a product owner's control is challenging, and practitioners rely on emission factors (EFs)─estimations of GHG emissions per unit of activity─to model and estimate indirect impacts. However, the current practice of manually selecting appropriate EFs from databases is time-consuming and error-prone and requires expertise. We present an AI-assisted method leveraging natural language processing and machine learning to automatically recommend EFs with human-interpretable justifications. Our algorithm can assist experts by providing a ranked list of EFs or operating in a fully automated manner, where the top recommendation is selected as final. Benchmarks across multiple real-world data sets show our method recommends the correct EF with an average precision of 86.9% in the fully automated case and shows the correct EF in the top 10 recommendations with an average precision of 93.1%. By streamlining EF selection, our approach enables scalable and accurate quantification of GHG emissions, supporting organizations' sustainability initiatives and progress toward net-zero emissions targets across industries.

Political Perspective in Geo-Map Analysis: What is the Role of World Governance Indicators in Reducing Greenhouse Emission in ASEAN Countries?

This study aims to analyze whether world governance indicators can reduce greenhouse gas emissions in ASEAN countries. The data used is panel data of 11 ASEAN member countries in the period 2013 – 2020 with justifications before and after the Paris Agreement with panel regression methods and mapping using orange data mining geo maps. Empirical results show that political stability no violence, government effectiveness, corruption control, and the implementation of the Paris Agreement are able to reduce the amount of greenhouse gas emissions. It was concluded that the success of the Paris Agreement will occur when committed countries dare to carry out economic transformation. However, to achieve this, significant adjustments are needed, and all parties must have a uniform vision for achieving the net zero carbon target.

Detection and Quantification of Vegetation Losses with Sentinel-2 Images Using Bi-Temporal Analysis of Spectral Indices and Transferable Random Forest Model

The precise spatially explicit data on land cover and land use changes is one of the essential variables for enhancing the quantification of greenhouse gas emissions and removals, which is relevant for meeting the goal of the European economy and society to become climate-neutral by 2050. The accuracy of the machine learning models trained on remote-sensed data suffers from a lack of reliable training datasets and they are often site-specific. Therefore, in this study, we proposed a method that integrates the bi-temporal analysis of the combination of spectral indices that detects the potential changes, which then serve as reference data for the Random Forest classifier. In addition, we examined the transferability of the pre-trained model over time, which is an important aspect from the operational point of view and may significantly reduce the time required for the preparation of reliable and accurate training data. Two types of vegetation losses were identified: woody coverage converted to non-woody vegetation, and vegetated areas converted to sealed surfaces or bare soil. The vegetation losses were detected annually over the period 2018–2021 with an overall accuracy (OA) above 0.97 and a Kappa coefficient of 0.95 for all time intervals in the study regions in Poland and Norway. Additionally, the pre-trained model’s temporal transferability revealed an improvement of the OA by 5 percentage points and the macroF1-Score value by 12 percentage points compared to the original model.

Calculating the Carbon Footprint of Urban Tourism Destinations: A Methodological Approach Based on Tourists’ Spatiotemporal Behaviour

This study investigates the influence of urban tourists’ behaviour on the environmental performance of a destination, particularly in terms of carbon emissions. Tourist-related emissions are shaped by their choices and behaviours, impacting the overall carbon footprint of the locations they visit. To assess this impact, we introduce a methodology for quantifying greenhouse gas emissions linked to tourists’ energy consumption. This approach considers key tourism components—activities, accommodation, and transportation—analysing their roles in emissions across a trip’s temporal and spatial dimensions. By integrating tourists’ spatiotemporal behaviour with emissions data, our framework offers insights that can support local climate-responsive urban and tourism policies. We empirically apply the proposed model to the destination of Donostia/San Sebastián (Spain), where the primary travel sequences of visitors are analysed. We utilise cartographic techniques to map the environmental footprints of different tourist profiles, such as cultural and nature tourists. The findings indicate that visitors primarily motivated by nature and outdoor recreation constitute the segment with the highest greenhouse gas emissions (with a minimum footprint of 30.69 kg CO2-equivalent per trip), followed by cultural tourists, and finally, other categories of visitors. The results highlight the practical applications of the proposed model for sustainable tourism management, providing valuable guidance for urban planners and policymakers in mitigating the environmental impacts of tourism.

Evaluating machine learning models for greenhouse gas emissions prediction in diversified semi‐arid cropping systems

AbstractClimate‐smart agriculture emphasizes improving crop production while mitigating greenhouse gas (GHG) emissions. In recent years, machine learning (ML) models have been increasingly used to unlock complex problems in agriculture, such as an enhanced understanding of drivers regulating GHG emissions. However, these models have not been evaluated widely, specifically quantifying GHG emissions in arid and semi‐arid cropping systems. We evaluated soil GHG emissions in irrigated crop rotations with and without cover cropping using field‐based measurements and five ML models, that is, decision tree, random forest (RF), gradient boosting (GB), bagging regressor, and XGB booster. The ML model was trained and evaluated using &gt;1600 data points of daily carbon dioxide (CO2), and nitrous oxide (N2O) measurements from four cover cropping practices (no‐cover crop and grass–brassica, grass–legume, and grass–brassica–legume cover crops) along with environmental (air temperature, precipitation), soil (moisture, temperature), and crop and management (cover crop carbon content, irrigation) variables and CO2 and N2O emissions was predicted. The RF model predicted CO2 emissions (R2 up to 0.68) more accurately, and N2O emissions were predicted better by the RF and GB models than other models with R2 up to 0.56 and 0.55, respectively. Irrigation was the most critical driver of CO2 emissions, and air temperature was the main driver of N2O emissions. ML models can effectively estimate GHG emissions using simple predictors, such as field management and environmental parameters, and help in designing climate‐smart management in arid and semi‐arid regions.

A Study on Measures to achieve Net Zero and RE100 goals in Urban Water Cycle System

Urban water cycle systems(UWCS), including water treatment facilities, distribution facilities, sewers, and wastewater treatment facilities, are energy intensive and significant source of greenhouse gas (GHG) emissions, making the reduction of GHG emissions and the transition to eco-friendly energy essential. This study identifies specific GHG emission sources at each stage of the UWCS and proposes detailed methods to achieve a 40% reduction in GHG emissions, implement RE100, and attain Net Zero by employing insets and offsets. This study develops scenarios for insets and offsets based on the baseline process of the UWCS, and investigates potential pathways to reduce GHG emissions by quantifying emissions from each process. Internal insets, which are self-implemented and technical measures, are prioritized, while external offsets are applied to compensate for the remaining emissions. Internal insets include the application of anaerobic digesters and combined heat and power(CHP), improvements in energy efficiency of equipment, reduction in water pipe leakage, implementation of water footprint labeling, and installation of on-site photovoltaic system. External offsets comprise renewable energy certificates(REC), power purchase agreements(PPA), green hydrogen fuel for vehicles, natural sequestration improvement, and emission trading system. GHG emissions at each stage within the UWCS are quantified using modeling software. Based on these results, the effectiveness of insets and offsets in achieving a 40% GHG emissions reduction, Net Zero, and RE100 goal is analyzed. The baseline total GHG emissions for the UWCS are estimated at 4,732.8 tCO2eq/yr, of which 56.8% is identified as targets for internal insets, and the remaining 43.2% is reduced through external offsets. A 40% GHG reduction can be achieved through internal insets, and Net Zero can be attained by incorporating additionally applying external offsets. The total power demand of UWCS facilities and equipment is calculated as 572.8 kW. Renewable energy is generated through anaerobic digesters and CHP(116.1kW) as well as on-site PV(395.0 kW), while RE100 compliance is achieved by securing an aditional 61.7 kW through REC/PPA. Achieving Net Zero and RE100 requires prioritizing strategies for insets, offsets and efficient resource allocation. For this, the technical feasibility and self-implementation potential of reduction efforts and the external conditions for offsets, should be carefully reviewed to optimize implementation strategies. GHG reduction and renewable energy utilization in the UWCS are key priorities for addressing the climate crisis and achieving sustainable water resource management, requiring technological innovation and institutional support. The comprehensive and systematic application of GHG insets and offsets is the optimal approach to achieving these goals. Furthermore, modeling software serves as a key tool for quantifying GHG emissions and formulating concrete, viable GHG reduction strategies. In addition to the technical and institutional approaches proposed in this study, achieving Net Zero and implementing RE100 requires the integrated consideration of economic factors in the future.

The Impact of Climate Change on Global Environmental Security: Causes and Solutions

Globalclimatechangesinthe Twenty-FirstCenturyarethe resultof the Industrial Revolution, the consumption of Natural Resources, unplanned urbanization and damage to the components of the atmosphere by increasing the concentration of greenhouse gases. Theconcept of State Security is no longer limited to protecting external borders only, but has been associated with threats to the environment and climate change, and environmental degradation has become a tangible reality. therefore, the term environmental security has emerged, which is an important means of achieving the security and stability of states and Man together. This study came to shed light on the climate changes of the Twenty-First Century and compare them with the twentieth and nineteenth centuries to identify the risks and challenges facing the world due to climate change, and the research concluded that the climate change in the world in the Twenty-First Century is the largest compared to the rest of the centuries, the evidence of this is the temperature anomaly and the unprecedented rise in the Arctic, where the temperature rise reached 3-4 m° from other centuries. The research also found that the world's countries are not committed to the amount of greenhouse gas emissions and did not realize the risks of climate change until 2007.The most important conclusion sreached by the research are that there may be an opportunity to reduce these effects, especially if the global agreement was made to reduce greenhouse gases and control emissions based on renewable sources and work on the development and investment in these projects, especially after the global attention and the holding of international conferences, including the Paris conference.

A Techno-Ecological Transformative Approach of Municipal Solid Waste Landfill in Upper-Middle-Income Countries Based on Energy Recovery

This paper presents several transformative scenarios of municipal solid waste landfilling sites from technical and ecological points of view, applicable to upper-middle-income countries, as per the classification of the World Bank. Our approach is based on numerical simulations of greenhouse gas (GHG) emissions in 2012 and 2025 and numerical simulations of methane emissions in a selected landfilling site, Oued Smar, in Algiers (Algerian capital city), according to the LandGem and IPCC models. Business-as-usual, landfill gas flaring, and electricity generation scenarios are considered in the numerical simulations. Finally, a novel metric dividing the recoverable electrical power by the amount of avoided greenhouse gas emissions is suggested. This paper reveals that the LandGem results were closer to reality and exhibited slightly higher values of energy recovery. A novel “techno-ecological” metric, computed as the ratio of energy recovery to avoided amounts of GHG emissions, was suggested for controlling landfill transformation. Accordingly, transitioning from uncontrolled landfilling to energy recovery could reduce GHG emissions by up to 99.87%, with a generated power of 0.89 W per ton of CO2-eq avoided by 2025.

PCF-RWKV: Large Language Model for Product Carbon Footprint Estimation

As global climate change intensifies, assessing product carbon footprints serves as a foundational step for quantifying greenhouse gas emissions throughout a product’s lifecycle, forming the basis for achieving sustainability and emission reduction goals. Traditional lifecycle assessment methods face challenges such as subjective boundary definitions and time-consuming inventory construction. This study introduces PCF-RWKV, a novel model based on the RWKV architecture with task-specialized low-rank adaptations (LoRAs). Trained on carbon footprint datasets, the model minimizes memory use and data interference, enabling efficient deployment on consumer-grade GPUs without relying on cloud computing. By integrating multi-agent technology, PCF-RWKV automates the creation of lifecycle inventories and aligns production processes with emission factors to calculate carbon footprints. This approach significantly improves the efficiency and security of corporate carbon footprint assessments, providing a potential alternative to traditional methods.

INOVASI KERAJINAN KERTAS DI THE GRIYA LOMBOK UNTUK MENDUKUNG PARIWISATA RAMAH LINGKUNGAN MENUJU NET ZERO EMISSION INDONESIA 2060

Net Zero Emission is a condition where the amount of greenhouse gas emissions released does not exceed the amount of emissions that can be absorbed by the earth. The program aims to minimize environmental pollution that has the potential to cause global warming and other natural disasters. One of the efforts to support the program is to apply paper craft innovations found at The Griya Lombok, which recycle paper into high-quality paper craft art. In addition, the application of these paper craft innovations in Indonesia can also support environmentally friendly tourism. To achieve this, the role of young intellectuals is needed to disseminate and provide education about the positive impact of paper craft innovation at The Griya Lombok on the environment. In order to increase the smoothness of achieving environmentally friendly tourism and Net Zero Emission in Indonesia, support from the Indonesian government is also needed. This scientific paper aims to find out the potential of paper crafts in supporting the concept of environmentally friendly tourism and Indonesia's Net Zero Emission 2060 program, as well as the role of young intellectuals to achieve these goals. The author uses qualitative research methods to collect information on site directly. The recycling of waste paper into craft art can reduce tree cutting, which is one of the causes of more greenhouse gas emissions than the earth can absorb. In addition, support from the Indonesian government for the paper craft industry can facilitate the path to successfully achieving the concept of environmentally friendly tourism and the Net Zero Emission program in Indonesia.

Exploring Crop Production Strategies to Mitigate Greenhouse Gas Emissions Based on Scenario Analysis

In the context of global climate change and carbon neutrality goals, agriculture has emerged as a major source of greenhouse gas (GHG) emissions, and faces the critical challenge of reducing emissions while ensuring food security. However, existing research has rarely focused on dynamic simulation and scenario-based analysis of optimised agricultural layouts and their impact on GHG emissions. Taking the three northeastern provinces (Heilongjiang, Jilin, and Liaoning) of China as the study area, this study quantifies GHG emissions from major grain crops and employs time-series analysis and machine learning methods to conduct a scenario analysis, including three scenarios (Business as Usual, Sustainable Optimisation, and Ecological Priority). Specific policy implications are proposed for optimising agricultural layouts and mitigating GHG emissions. The results indicate that GHG emissions in Northeast China primarily stem from methane emissions in rice cultivation and nitrous oxide emissions from fertiliser use. A scenario analysis reveals that the “Sustainable Optimisation” scenario reduces GHG emissions by 22.0% through optimised planting layouts while maintaining stable crop production. The “Ecological Priority” scenario further enhances emission reductions to 25.2% by increasing the share of low-emission crops, such as corn, and reducing high-emission crops, such as rice. The study provides a practical reference for promoting the low carbonisation of agriculture, and demonstrates that optimising planting layouts and production structures can simultaneously achieve food security and climate change mitigation.