Estimates Of Carbon Emissions Research Articles

Estimation of Carbon Emissions and Empirical Analysis of Carbon Compensation in Rural Tourism Development

Estimation of Carbon Emissions and Empirical Analysis of Carbon Compensation in Rural Tourism Development

A Local-First Approach for Green Smart Contracts

Shared code in blockchains, known as smart contracts , stands to replace important parts of our digital governance and financial infrastructure. The permissionless execution of smart contracts is tightly coupled to cryptocurrencies and Proof-of-Work blockchains. As a result, smart contracts inherit the environmental impact of Proof-of-Work blockchains, such as its energy consumption, carbon footprint, and electronic waste. The four concepts of relaxed consistency, strong identities, probabilistic consensus, and the use of liabilities instead of assets may change the status quo. This work explores the integration of these concepts to decouple smart contracts from Proof-of-Work blockchains. By means of a local-first approach, which may expose users to inconsistent ephemeral contract states, the architecture of smart contracts can be transformed to become green. Because such contract states may be dropped, we base the interactions between users on liabilities. We propose a novel paradigm for smart contract architectures, named Green Smart Contracts, that is based on a local-first approach. Furthermore, we present and implement a prototype solution for this paradigm. We validate the need for a mechanism to resolve consistency violations by replaying the contract calls of a real smart contract. Our simulation shows that violations occur more often (13% of contract invocations) when using liabilities than when using a traditional blockchain (3% of contract invocations). However, we additionally validate that they can be avoided using a consensus mechanism, and our experiments show that a publish-subscribe messaging pattern uses the fewest messages to do so, though it may not be applicable for use cases that disallow the inherent imbalance in the messaging between peers. Our carbon emission estimation shows that a Green Smart Contract approach lowers carbon emissions by 52.31% when compared with the messaging behavior of a typical peer-to-peer blockchain with 1000 nodes.

Performance evaluation of low-carbon development at key nodes in the maritime supply chain

ABSTRACT How to effectively monitor and evaluate the low-carbon level of the key nodes in the maritime supply chain is a important challenge for the sustainable development of ocean. This article adopted the ‘bottom-up’ and ‘top-down’ estimation methods comprehensively to measure the carbon emissions of ‘ship-port’ system. A new cross-efficiency network DEA (Data envelopment analysis) model has been established to evaluate the low-carbon efficiency of the ‘ship-port’ system. Finally, this article takes 17 listed ports along the coast of China as examples for case analysis. The main contributions are as follows: First, this paper adds a methodology for monitoring carbon emissions from ships using Automatic Identification System (AIS) data. Second, compared with the European monitoring, reporting, and verification (MRV) system, the estimation and assessment of carbon emissions from ships has been expanded to the ‘ship-port’ system. Third, the newly constructed model can better evaluate the low-carbon performance of the system.

The Optuna–LightGBM–XGBoost Model: A Novel Approach for Estimating Carbon Emissions Based on the Electricity–Carbon Nexus

With the challenge posed by global warming, accurately estimating and managing carbon emissions becomes a key step for businesses, especially power generation companies, to reduce their environmental impact. Optuna–LightGBM–XGBoost, a novel power and carbon emission relationship model that aims to improve the efficiency of carbon emission monitoring and estimation for power generation companies, is proposed in this paper. Deeply exploring the intrinsic link between power production data and carbon emissions, this model paves a new path for “measuring carbon through electricity”, in contrast to the emission factor method commonly used in China. Unit data from power generation companies are processed into structured tabular data, and a parallel processing framework is constructed with LightGBM and XGBoost, and optimized with the Optuna algorithm. The multilayer perceptron (MLP) is used to fuse features to enhance prediction accuracy by capturing characters that the individual models cannot detect. Simulation results show that Optuna–LightGBM–XGBoost can achieve better performance compared to existing methods. The mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and coefficient of determination (R2) of the model are 0.652, 0.939, 0.136, and 0.994, respectively. This not only helps governments and enterprises to develop more scientific and reasonable emission reduction strategies and policies, but also lays a solid foundation for achieving global carbon neutrality goals.

Sustainable Energy Consumption Analysis through Data Driven Insights

Energy is the backbone of our society, supporting our daily activities and driving progress. It plays a crucial role in shaping our modern way of life. The future of global energy consumption is influenced by many factors, including demographics, economic dynamics, technological developments, political actions, environmental demands and geopolitical considerations. As the world's population continues to grow and urbanize, the demand for energy is increasing. At the same time, rapid technological innovations are shaping the energy landscape and changing production, distribution and consumption patterns. In the midst of this development, it is very important to optimize energy consumption, accurately anticipate needs, curb climate change, limit emissions of greenhouse gasses, fight against pollution and promote sustainability. This study includes an in-depth analysis of historical consumption trends, assessing the multiple benefits of renewable energy integration, estimating carbon emissions, formulating practical policy recommendations and providing empirically informed insights. The work is based on various data obtained from platforms such as Kaggle and using advanced visualization techniques such as Power BI dashboards. The study provides invaluable perspectives on future energy needs, the penetration of renewable sources into the energy mix, and the strategic needs to achieve sustainable energy use.

Spatiotemporal evolutions and reasons of land-use carbon emissions in counties, Shaanxi Province, China

Global warming caused by factors such as the expansion of construction land poses a major threat to the sustainable development of China. As an important component of China's low-carbon development strategy, there is a relative lack of analysis of the spatial and temporal evolution of land-use carbon emissions and the influencing factors at county-level. In this study, we employed Emission-Factor Approach to estimate carbon emissions from land use in 107 counties, Shaanxi province, China. Our findings revealed construction land were the primary contributors to carbon emissions, showing a substantial increase from 2000 to 2020. There was positive spatial autocorrelation in carbon emissions among counties, forming distinct aggregation patterns around the City of Xi'an and both the southern and northern regions of Shaanxi. By utilizing the Spatial Durbin Error Model (SDEM), demographic factors emerged as key drivers of carbon emissions, indicating the significance of addressing population concentration to curb emissions. Furthermore, promoting coordinated development and adjusting the economic structure in different counties can mitigate both population concentration and carbon emissions. Emphasizing industrial development and investments can also effectively suppress carbon emissions. Additionally, managing transportation-related emissions can be achieved by enhancing public transportation services and regulating private car usage.

County-level intensity of carbon emissions from crop farming in China during 2000–2019

Agriculture is an important contributor to global carbon emissions. With the implementation of the Sustainable Development Goals of the United Nations and China’s carbon neutral strategy, accurate estimation of carbon emissions from crop farming is essential to reduce agricultural carbon emissions and promote sustainable food production systems in China. However, previous long-term time series estimates in China have mainly focused on the national and provincial levels, which are insufficient to characterize regional heterogeneity. Here, we selected the county-level administrative district as the basic geographical unit and then generated a county-level dataset on the intensity of carbon emissions from crop farming in China during 2000–2019, using random forest regression with multi-source data. This dataset can be used to delineate spatio-temporal changes in carbon emissions from crop farming in China, providing an important basis for decision makers and researchers to design agricultural carbon reduction strategies in China.

Managing the risks against carbon neutralization for green maritime transport

Maritime transport plays a vital role in global trade. With low transport costs and high service reliability, approximately 80% of products are transported by sea. The international nature of maritime transport supports globalized production and consumption patterns. As a result, maritime transport is uniquely influenced by the convergence of environmental, geopolitical, and social factors. One of the environmental factors linked to maritime transport is emissions. Although many steps have been taken to reduce emissions within the scope of carbon neutrality by implementing new technologies and policies, the volume of emissions produced in maritime transport is still relatively high and threatens global sustainability. This research attempts to estimate the volume of carbon emissions produced by international shipping using the Simple Exponential Smoothing method. After highlighting the situation, this research aims to determine the risks that cause carbon emissions and which should be prioritized using the Graph Theory and Matrix Approach. This approach draws attention to the fact that a scientific basis has remained in the background. This study emphasizes the scientific basis of the method used to estimate carbon emissions, ensuring that geopolitical and social risks are taken into account as well as environmental factors. Thus, it offers a more comprehensive perspective to help managers and decision-makers in developing emission reduction strategies, contributing to the greening of maritime transport.

Low-Carbon Mixed Traffic Route Recommendation for Community Residents Based on Multilayer Complex Traffic Network

With the proposed “carbon peaking” and “carbon neutral” goals in China, the transportation sector, as the second largest consumer of oil and a major producer of greenhouse gases, is a critical area for energy efficiency and emission reduction actions. However, few studies have focused on the effective combination of solving residents' commuting challenges and low-carbon travel. In this paper, by extracting real traffic flow data from taxi and bike-sharing trajectory data, a multilayer complex traffic network is formed to realize an interactive visual exploration of urban traffic patterns. Based on this network a low-carbon travel route recommendation is implemented using a modified genetic algorithm to reduce personal carbon emission and travel costs. Meanwhile, the trip chain level carbon emission estimation method is defined for city streets and recommended routes. With the integration of the above algorithms, a visual analytics system is designed and implemented to support the joint exploration of urban traffic patterns and the street carbon emission distribution, low-carbon mixed traffic route recommendations for inter-community commuting, and optimization of low-carbon recommended routes by adjusting bike stations. Take the taxi and bike-sharing trajectory data in Xiamen, China as an example, an evaluation analysis of the system shows that the method is effective in reducing commuting costs for community residents while reducing personal travel carbon emission.

Predictive models of embodied carbon emissions in building design phases: Machine learning approaches based on residential buildings in China

As one of the major architectural forms, residential building construction consumes considerable resources, and it has been regarded as a crucial factor for the implementation of carbon peaking and neutrality targets. Estimation of carbon emissions is essential for making informed decisions regarding building design and carbon reduction measures. Some studies have established predictive models for building operational carbon emissions. However, there are relatively few studies concerning the prediction of embodied carbon emissions. This study established a dataset containing 850 residential building samples. Based on the trade-offs and combinations of different explanatory variables, including building features and material use, this study developed two-phase models. These models, incorporating six scenarios, predicted embodied carbon intensities using twelve machine learning algorithms. For the planning design phase, an extremely randomized tree model using the building height, structural form, seismic fortification intensity, delivery form, geographical region, and material cost as inputs was favored considering R2 as 0.821 on the testing dataset. For the preliminary design phase, an extreme gradient boosting model using both building features and material intensities provided superior performance with R2 and MAPE values of 0.917 and 0.038, respectively. The optimal models and relevant feature importance analysis can be helpful for efficiently predicting embodied carbon emissions, and therefore, facilitate low-carbon designs.

An Outsized Contribution of Rivers to Carbon Emissions From Interconnected Urban River‐Lake Networks Within Plains

AbstractUrban aquatic ecosystems in plains are often subject to extensive anthropogenic pollutant inputs and have prolonged times for pollutant degradation, potentially leading to diverse carbon emission patterns. This study explored carbon emission patterns and underlying mechanisms in Ge Lake and its tributaries, located in an urban area within a plain in China. The results revealed that carbon emissions from rivers were significantly higher than those from the downstream lake. Spatial interpolation analysis further revealed that CO2‐eq emissions from a 1‐km2 river area can be equivalent to those from an area as large as 86‐km2 of the downstream lake. Rivers are the gateway for the entry of organic compounds, often carrying substances that are readily biodegradable. As the river water moves slowly, these compounds accumulate and undergo degradation in rivers before they reach downstream lakes. The findings may benefit the estimates of carbon emissions in these regions with greater precision.

Toward more accurate estimates of carbon emissions from small reservoirs

AbstractBecause of their abundance and high emissions rates, small reservoirs (< 0.01 km2) can be important emitters of the greenhouse gases carbon dioxide and methane. However, emissions estimates from small reservoirs have lagged those of larger ones, and efforts to characterize small reservoir emissions have largely overlooked variations in emissions pathways, times, and locations. We intensively sampled four small reservoirs in Georgia, USA, during the summer to quantify the contribution and spatiotemporal variability of different emissions pathways (CO2 and CH4 diffusion, CH4 ebullition). We used these data to evaluate the efficiency and accuracy of different sampling schemes. Every emissions pathway was dominant in one reservoir on one sampling day, and excluding ebullition caused misestimation between −89% and −15% of the total flux. Sampling only once daily caused misestimation between −78% and 45%, but sampling twice or just after dawn (07:00 h) reduced error. Sampling four or fewer locations caused misestimation between −85% and 366%, and our results indicated that 6–20 sampling locations may be needed for reasonable accuracy. The floating aquatic macrophyte Wolffia sp. (duckweed) appeared to exert control over emissions variability, and the consequences of not accounting for variability were greater in a duckweed‐covered reservoir. Our results indicate that sampling only at 10:00 h (modal sampling time of prior efforts) may lead to the erroneous conclusion that reservoirs with high photosynthetic biomass are CO2 sinks rather than sources. Improving estimation accuracy by accounting for within‐reservoir variation in emissions will facilitate more strategic management of these abundant, anthropogenic ecosystems.

Estimation of energy demand and carbon emissions for the road transport sector: A case study of Douala, Cameroon

Road transport is one of the main causes of air pollution around the world. Nowadays, the notion of sustainable development is one of the major pillars for a healthy environment. However, faced with its major challenges, it is more than necessary and important to understand a set of elements which contribute to better understanding the level of planning for sustainable road transport. This study aims to estimate energy demand and greenhouse gas emissions, namely from road CO2 transport, for the city of Douala in Cameroon from 2010 to 2035. In order to achieve our objective, we used the Long range Energy Alternative Planning (LEAP) model. Based on the LEAP model, the estimates were evaluated based on three scenarios Business As Usual (BAU), Energy Optimization and Mitigation (EOM) and Sustainability Mobility (SM). The aim of this work was to identify suitable potential policies with a view to reducing energy consumption and emissions carbon dioxide (CO2) for road transport in Douala. The results tell us that the EOM and SM scenarios have advantages over the BAU scenario with the SM scenario being more optimistic than the other two scenarios. In addition, the SM scenario has a growth rate of 87.408 % in energy demand smaller than the other two scenarios BAU and EOM respectively 151.598 % and 132.073 %. We also note that the SM scenario contributed to a reduction of 60.661 kilo ton emissions of CO2 less than the BAU scenario. At present, with a view to reducing energy consumption and reducing carbon emissions, road vehicles in circulation in the city of Douala should emphasize structural optimization measures through the use of clean energy by promoting the use of biofuels in mass transport, eliminating old cars and finally considering improving energy efficiency through the implementation of technological development in the automobile sector. Policy makers must take this study into consideration in order to better integrate the notion of energy sustainable road transport.

Quantifying uncertainty in carbon emission estimation: Metrics and methodologies

Carbon emissions are a significant driver of global climate change in today's world. A central concern in discussing carbon emissions is the level of uncertainty associated with them. This study aims to assess the feasibility of using the Mean Squared Error (MSE) as a point estimation measure and confidence interval (CI) as an interval estimation measure to quantify the uncertainty surrounding carbon emissions. To achieve this goal, the bootstrap and Markov Chain Monte Carlo (MCMC) sampling methods were used, utilizing both classical and Bayesian inference methods to uncover the true parameter value.In the context of Bayesian inference, a 2-chain MCMC method proved to be the optimal choice for generating posterior distributions and accurately estimating the true parameter of the distribution θ. The analysis also shows that, while a CI is valuable as an evaluative measure, it does not inherently provide a quantified form of uncertainty and should not be used for quantitative uncertainty assessment. Instead, the Relative Standard Error (RSE) emerges as a promising quantitative measure for capturing the uncertainty of θ, while the percentage uncertainty offers a qualitative perspective. These combined measures provide a comprehensive toolkit for computing and communicating the uncertainty in carbon emission and emission factor values. This reinforces a move towards incorporating transparent uncertainty metrics, ensuring that stakeholders have access to reliable and accurate carbon emission values.

Improving urban CO2 spatial distribution modelling using multi-source data

Urban areas contribute approximately 70% of the total anthropogenic CO2 emissions, making them the primary focus of global carbon monitoring. However, accurate modelling of urban meteorology is challenging because of complex artificial landscapes. Reducing errors in urban meteorological simulation and improving the accuracy of modelling the spatial distribution of urban CO2 is of great significance for the “top-down” CO2 emissions estimation in urban areas. In this study, local urban datasets were constructed based on multiple data sources to overcome the limitation of insufficient information from a single data source. In the development of urban datasets, grid-by-grid data processing is realized, and the evaluation results show that the developed urban datasets are greatly improved compared with the default. The developed urban datasets were applied to WRF-Chem with 1-km spatial resolution, and the simulated column-averaged dry air mole fraction of CO2 (XCO2) was verified with the EM27/SUN observed data considering the slant observation path. The results show that urban datasets strongly influence the spatial distribution modelling of XCO2, depending on the state of the atmosphere near the surface, especially wind velocity. Accurate local urban datasets can effectively reduce the bias in urban XCO2 spatial distribution modelling and improve the capability of the atmospheric CO2 transport model in urban carbon emission estimation.

Quantifying the greenhouse gas emissions of New Zealand households’ food purchases: An analysis by sociodemographic variables

New Zealand has committed to a 50% reduction in greenhouse gas emissions (GHGEs) from 2005 levels by 2030. Dietary changes within New Zealand could simultaneously improve population health and contribute towards the nation’s emissions reduction target, as agricultural emissions are estimated to account for half of New Zealand’s GHGEs(1). This research aimed to quantify the GHGEs associated with household purchases of major food groups in New Zealand and identify the sociodemographic characteristics that are associated with per capita household dietary emissions. Household dietary emissions were estimated using the NielsenIQ Homescan(R) consumer panel — a large sample of households within New Zealand who report purchasing data of take-home food and beverages. The sample is nationally representative in terms of broad geographical regions and selected key demographic characteristics. Carbon emission estimates were assigned to 1,908,485 total food and beverage purchases from 1,775 households over one year (2019) using a process-based life cycle assessment (LCA) dataset initially constructed in the United Kingdom (UK) and adapted for New Zealand(2). This LCA dataset contains estimates of greenhouse gas emissions generated over the life cycle of the production of food products from the following stages: farming and processing, transit packaging, consumer packaging, transport, warehouse and distribution, refrigeration, and overheads. Greenhouse gas emissions are expressed in kg of carbon dioxide equivalents per kg of food product over a 100-year time horizon. Total emissions from purchases of major food groups were then estimated. Multiple linear regression was used to examine the relationships between household variables and per capita dietary emissions. Overall purchases of red and processed meat (35%) and dairy products (19%) were responsible for the greatest proportion of emissions. The age group of the primary household shopper as well as household size were predictors of per capita dietary emissions — households with primary shoppers > 65 years had, on average, 33% (95% CI: 20% to 49%) higher per capita dietary emissions, compared to households with primary shoppers 34 years; and every additional household member was associated with, on average, 11% (95% CI: 9% to 13%) lower per capita dietary emissions. We have shown in this large representative sample of New Zealand households that purchases of just two food groups — red and processed meat, and dairy — were responsible for approximately half of dietary greenhouse gas emissions. Larger households had lower per capita dietary greenhouse gas emissions, and older shoppers had relatively higher greenhouse gas emissions. Whilst similar associations have been reported elsewhere more research is needed to confirm these latter findings. With enhanced understanding of the observed association between age of a household’s primary shopper and per capita dietary emissions, interventions may be devised that encourage shoppers to purchase lower-emitting foods, particularly less meat and dairy.

Carbon reduction effects of digital financial inclusion: Evidence from the county-scale in China

All countries in the world are faced with varying degrees of environmental pollution problems and carbon reduction responsibilities. In the case of China, digital transformation emerges as a new growth catalyst for the future economy and a pivotal avenue for optimizing resource allocation, enhancing energy structure, and reducing energy dependence for development. Therefore, this study delves into the carbon reduction effect of digital financial inclusion and explores its pathways. It focuses initially on the counties of the Yangtze River Delta in China, utilizing nighttime light data to estimate carbon emissions and scrutinizing the spatial and temporal distribution characteristics of emissions. Empirical results reveal that digital financial inclusion positively influences carbon emission reduction, with notable spatial and temporal variations in the emission reduction effect. Furthermore, the study uncovers that digital financial inclusion contributes to reducing carbon emissions through the optimization of industrial structure and the promotion of technological innovation. These research findings offer a scientific foundation for guiding the government in developing digital finance to attain local carbon emission reduction targets. Concurrently, local governments should leverage the role of digital financial inclusion in optimizing industries and driving technological innovation to propel the low-carbon, sustainable development of China.

Virtual Learning Decreases the Carbon Footprint of Medical Education.

The environmental impact of holding in-person academic conferences and continuing medical education (CME) programs can be significant. In-person conferences provide a unique social and professional platform to engage in networking and foster professional development; however, there is an opportunity for hybrid and virtual platforms to provide CME for broader audiences looking to improve their clinical skills and strengthen their knowledge base. This study seeks to describe the reduction in carbon emissions associated with a webinar hosted by an online dermatology-focused medical education platform. This cross-sectional study used the location of deidentified virtual attendees of a webinar to predict the carbon emissions produced if attendees had instead traveled to the location of the most recent Integrative Dermatology Symposium (Sacramento, CA). Following collection of each virtual attendee's location, the mode of transportation was predicted on the basis of each participant's distance to the conference. The estimated carbon emissions were calculated for 576 participants. The total estimated, unadjusted carbon emissions for both attendees predicted to fly or drive was 370,100kg CO2. The emissions produced per participant from those expected to fly to an in-person CME after adjusting for all additional passengers on every flight were 4.5kg CO2. The emissions produced per participant from those expected to drive were 42.7kg CO2. The use of a virtual CME webinar led to a significant reduction in travel-related carbon dioxide emissions when compared to running the same program in-person event. When accounting for all passengers traveling via plane on any flight, driving to an event produced more emissions per participant than flying.

Estimating carbon emissions from thermal power plants based on thermal characteristics

Carbon emissions from thermal power plants (TPPs) are a significant source of anthropogenic carbon and a priority for carbon reduction efforts. The accurate estimating of carbon emissions from TPPs is essential research in the field of carbon reduction. In this study, we take TPPs located in the United States as examples to discuss the relationship between the thermal characteristics of the power plant and its annual carbon emissions. Our data consists of land surface temperature (LST) product generated from Landsat 8 thermal data, LST product from ERA5-Land reanalysis dataset, and carbon emission data reported by the U.S. Energy Information Administration (EIA). Based on pertinent research of urban heat islands, we propose the concept of thermal characteristic index (TCI), which can provide a quantitative description of power plant thermal characteristics. Furthermore, we convert TCI to annual thermal characteristic index (A-TCI) by annual end-point interpolation and Simpson integration of the TCI series to match the annual carbon emission data and then establish a TCI-based annual carbon emission estimation model (TACEE-Model). Using the TACEE-Model, we predict the annual carbon emission values and evaluate the model’s performance. Experimental results indicate that the TACEE-Model exhibits great predictive capability, with the mean absolute percentage error (MAPE) of 0.0325, and the average prediction percentage error of 3.24%. The application of annual end-point interpolation and Simpson integration can significantly enhance the model’s prediction accuracy, resulting in a 51.42% reduction in MAPE. Overall, this research presents a new method for verifying annual carbon emissions from TPPs, which can contribute to the supervisory oversight of carbon emission reduction from TPPs.

Evaluating Carbon Emissions during Slurry Shield Tunneling for Sustainable Management Utilizing a Hybrid Life-Cycle Assessment Approach

The construction sector is one of the principal contributors to carbon dioxide emissions (CDEs) and has a vital role to play in responding to the issue of long-term environmental sustainability. This research proposes a process-based hybrid life-cycle assessment (LCA) method depending on a process-based LCA and an input–output LCA. The process-based hybrid LCA model provides a supplementary method to quickly estimate carbon emissions that are not considered in the system boundary due to the limitation of inventory data. The proposed hybrid method was applied to a carbon emissions assessment in a slurry shield tunnel. The results suggest that 93.88% of emissions are from materials. Of the materials contribution, 55.9% comes from steel and 34.55% arises from concrete. It has also been found that emissions during the tunneling stage are negatively correlated with the efficiency of tunnel construction. Recommendations for carbon emissions reductions in tunnel construction are provided for promoting sustainable transportation and management.