Human Carbon Emissions Research Articles

Balancing the development and carbon emissions in rural areas of China

Synergy relationship between human development and carbon emissions in rural areas is an important aspect of Sustainable Development Goals (SDGs). Chinese government is promoting a rural revitalization strategy towards 2050, but whether the development of rural industries and changes in farmers’ lifestyles will increase carbon emissions is a matter of concern. Here, this study obtained survey data from 322 villages of 27 provinces in China by face-to-face interviews, and the reliability of these data has been proven through T-test. Rural Human Development Index (RHDI) and rural carbon emissions have been evaluated, and “quadrant method” was conducted to clarify the relationship between RHDI and rural carbon emissions. Then, this study explored drivers of rural carbon emissions under the different kinds of villages. Results showed that the range of RHDI was 0.166–0.846, and 63.66% villages were at the low level. The average rural carbon emissions were up to 3.15E+03 t CO2 eq, and 28.26% villages were at the high level. However, villages with low RHDI and low carbon emissions accounted for the highest proportion (47.52%), and the increase of RHDI significantly caused more carbon emissions. 73.73% of carbon emissions came from people living, in which diesel of transportation and electricity were the main sources of rural carbon emissions, and the increase of population and area can significantly bring about more carbon emissions. Overall, the development level of Chinese rural area was relatively low, and the use of clean energy needs to be further optimized to arrive the goal of decoupling of economy and carbon emissions.

Intelligence and its effects on environmental decline: A worldwide analysis

The research investigates the relationship between intelligence quotient (IQ) and environmental degradation, aiming to understand how cognitive abilities influence environmental outcomes across different nations and time periods. The objective is to examine the impact of intelligence quotient (IQ) on environmental indicators such as carbon emissions, ecological demand, and the Environmental Kuznets Curve (EKC), seeking insights to inform environmental policy and stewardship. The study utilizes statistical techniques including Ordinary Least Squares (OLS), Two Stage Least Squares (2SLS), and Iteratively Weighted Least Squares (IWLS) to analyze data from 147 nations over the years 2000–2017. These methods are applied to explore the relationship between IQ and environmental metrics while considering other relevant variables. The findings reveal unexpected positive associations between human intelligence quotient and carbon emissions, as well as ecological demand, challenging conventional notions of "delay discounting." Additionally, variations in the Environmental Kuznets Curve (EKC) hypothesis are identified across different pollutants, highlighting the roles of governance and international commitments in mitigating emissions. The study concludes by advocating for the adoption of a "delay discounting culture" to address environmental challenges effectively. It underscores the complex interactions between intelligence, governance, and population dynamics in shaping environmental outcomes, emphasizing the need for targeted policies to achieve sustainability objectives.

Comparison of human health, energy consumption, and carbon emission with virgin and recycled filament in Fused deposition modeling printing

Fused deposition modeling (FDM) three-dimensional (3D) printing is commonly used by small factories, entrants, and families owing to its ease of use and speed. However, certain studies have shown that during the manufacturing process, particulate matter (PM) and volatile organic compounds (VOCs) are harmful to the respiratory system, and that carbon emissions associated with energy consumption can contribute to global warming. These studies considered only virgin filaments and not recycled filaments. Considering the importance of plastic recycling for achieving circular economy targets, the sustainability of recycled filaments much be investigated. This study used grey relational analysis to determine the multi-objective optimal printing parameters for virgin and recycled filaments on the human health and carbon emissions impacts and thus provide a reference for the “circularization” of filament materials. The experimental results indicate that the orientation angle, layer height, and heatbed temperature are the three most important parameters affecting human health and carbon emissions in recycled filaments. The optimal printing parameters showed an improvement of 20.24 % for all objectives, and the PM reached 54.02 %. Furthermore, based on a life-cycle assessment, the use of optimal printing parameters can decrease lifecycle emissions by 21.7 %.

The driving factors behind urban communities' carbon emissions in the selected urban villages of Jakarta, Indonesia.

As there is a high number of urban communities and residential activities like electricity uses, transportation, food consumption, garbage production, and knowledge on climate change and carbon emissions, these affect the resulting carbon emissions in the city. However, the studies learn about the association between household and human activities, socio-demographic characteristic, carbon emissions and climate change knowledge on urban communities carbon emissions at Jakarta are still rare. This study aimed to assess whether daily human behavior, demographic variables, climate change knowledge, and carbon emissions knowledge affecting carbon emissions of urban communities. A cross-sectional study design was performed via a structured questionnaire survey and a jejakkarbonku application towards 200 respondents who were living in the two selected urban villages of Jakarta, Indonesia (Jatinegara and East Cengkareng). The results revealed that the mean urban communities' carbon emissions in the region were 4.4 tonnes CO2 eq/th. Electricity consumption in the kitchen and dining room produced the most carbon emissions (38.6%), and waste disposal activity produced the least (0.68%). Urban carbon emissions affected by employment status and income level, but not according to food consumption, garbage production, climate change and carbon emission knowledge. Climate change and carbon emissions knowledge influenced by marital and employment status. Although not significant, in theory, these two types of knowledge contribute indirectly to carbon emissions. Further research on the low carbon diet, intervention studies to increase deep awareness on the carbon emissions and climate change is needed to develop strategies reducing carbon emissions at urban communities.

Impact of carbon emissions sources on life expectancy in Asia and the Pacific region

AbstractThis study aligns with the 2030 United Nations Sustainable Development Goals 3 and 11, which aim to “ensure healthy lives and promote well‐being for all at all ages” and “make cities and human settlements inclusive, safe, resilient, and sustainable”. The study objectives are to (i) explore the impact of different sources of carbon emissions on life expectancy; (ii) evaluate if the impact differs across income groups, and (iii) determine if life expectancy is persistent. It contributes to the health‐environment literature but differs from two standpoints. First, it uses two essential sources of carbon emissions: liquid and solid fuels. Second, it engages robust analyses comprising the full sample and sub‐samples of income groups to further interrogate this intrinsic relationship. For this purpose, an unbalanced sample of 36 countries from Asia and the Pacific region (2005–2010) is used, and for robust analyses, three econometric methods: bootstrap ordinary least squares (BOLS), instrumental variables (IV), and system generalized method of moments (GMM) are implemented. Empirical results reveal that: (1) liquid and solid fuel emissions exert significant negative outcomes, (2) liquid fuel emissions exhibit the most adverse effect, (3) life expectancy is persistent, and (4) impact of liquid and solid fuel emissions on life expectancy across the income groups is diverse. These findings suggest that the management authorities of Asia and the Pacific region need to devise prudent and effective policies concerning human health and carbon emissions while allocating sufficient budget to the health sector to rescue more lives and, thereby, lengthen the life expectancy in the region.

Energy transition in Africa: The role of human capital, financial development, economic development, and carbon emissions

The current energy structure of most economies must be upgraded to a renewable energy source (RES) to help mitigate ecological dilapidation and achieve sustainable development goals. The transition to RES has recently received attention from energy experts, international bodies and environmental scientists. However, there is contrasting evidence on what factors affect the evolution and deployment of RES, especially from the perspective of emerging African economies. Hence, the current analysis sought to explore the influence of economic development (ECD), carbon emission (CEM), human capital (HUC), financial development (FID), and the country’s risk factors, such as political risk (POR) and institutional quality (INQ) on the development of RES. The research employed a panel dataset from 1990 to 2020 and used the newest estimation approach of Cross-Sectional Augmented autoregressive Distributed Lag (CS-ARDL) to establish the long-run connection among the variables. The study confirmed the prevalence of the “energy-led ECD hypothesis”. In addition, the empirical findings indicated that RES deployment could be facilitated through ECD, CEM, FID and HUC. Moreover, the country’s risk factors, which include POR and INQ, had an inverse connection with the deployment of RES. Lastly, there is a unidirectional causality between CEM, FID, HUC, INQ and RES, while a directional causality exists between ECD, POR and RES. The paper highlights a variety of policy angles to help with the expansion of RES in emerging regions.

Does human capital complement sustainable development goals? Evidence from leading carbon emitter countries

In the quest to address economic and environmental issues, the UNDP emphasized generating synergy among various sustainable development goals (SDGs). In this regard, SDG4 (Quality of education) is the most crucial SDG, as human capital may help improve the quality of life and environment in the long run. Against this backdrop, this study explores human capital's direct and moderating effect on carbon emissions in nine leading carbon emitter nations from 1990 to 2018. We rely on a quantile approach to calculate elasticity coefficients and verify results using the cross-sectional-autoregressive-distributed lag (CS-ARDL) procedure. The results suggest that human capital leads to carbon emissions across quantiles when the direct impact of human capital on carbon emissions is assessed. Similarly, human capital helps navigate carbon emissions when interacting with industrial value-added and per-capita income. The association between the squared term of human capital and carbon emissions is negative across quantiles in leading carbon emitter nations. Besides investing in human capital, these nations need to intensify the usage of cleaner energy and advanced technologies because the elasticity coefficients of these variables are found to be negative across quantiles. We propose a sustainable growth roadmap in which interconnectedness among certain SDGs is considered.

Human Capital and Carbon Emissions: The Way forward Reducing Environmental Degradation

Many environmental problems are human induced, one of which is the change in atmospheric composition, a hot research topic in recent decades. This study aims to investigate the impact of human capital (HC) on carbon dioxide (CO2) emissions in Sri Lanka using time series annual data from 1978 to 2019. The time series data were examined for a unit root problem and an unknown structural break. An autoregressive distributed lag (ARDL) approach was employed to identify the long-run relationship between HC and CO2. The results confirm the long-term relationship between carbon emissions and human capital. As a unique finding of this research, the estimated coefficient of human capital to carbon emission is negative and statically significant, suggesting that a 1 percent increase in HC decreases carbon emissions by 1.627789 percent. The significance of this finding is that it can help achieve Sustainable Development Goal “13”, which focuses on combating climate change and its effects. The study indicated that building in HC by investing more in education helps to reduce carbon emissions in the long term. It reflects that human capital accumulation is linked to reduced environmental degradation due to lower CO2 emissions.

Net Zero: Actions for an Ocean-Climate Solution

It has long been understood that stabilizing the global climate requires human carbon emissions to converge toward net zero (Matthews and Caldeira, 2008). Analysis by the Intergovernmental Panel on Climate Change (IPCC) suggests delays in achieving net zero emissions, or a temperature increase of more than 1.5°C, will necessitate even stronger action: net negative emissions. A very large-scale opportunity for carbon sequestration would include yet unproven ocean-based carbon dioxide removal (CDR) technologies (Rogelj et al., 2018).

The nexus of carbon emissions, oil price volatility, and human capital efficiency

The corporate sector's active engagement is vital for achieving a net-zero future. It entails a sizeable investment in human capital to foster more conscious efforts to limit carbon emissions. Therefore it is critical to evaluate the nexus between carbon emissions and human capital efficiency. This paper analyzes the link between human capital efficiency and carbon emissions using a comprehensive sample of 5740 firms across eight countries spanning over ten years. Our findings show a negative relationship between investment in human capital and carbon emissions. We emphasize that optimal human capital efficiency can help limit emissions by fostering the transitions to renewable energy sources and the development of cognitive appreciation. The results remained robust for periods marked by booming and receding oil prices. These findings have important implications for optimizing firm performance while preserving sustainable development goals.

Who Contributes to the Sunk Costs of Motor Vehicle Carbon Emissions and Human Capital?

Most carbon emission reduction studies in transportation focus on the natural use of motor vehicles. However, little attention has been paid to fatal driving violations that lead to the sunk costs of human resources investment and motor vehicle carbon emissions in the manufacturing process of motor vehicles. This article aimed to determine if a driver would be seriously harmful to human capital investment and carbon emissions reduction. 1893 drivers in China were formally surveyed by questionnaire to find out the statistical characteristics. Results indicated that different drivers show significant differences in different driving violations dimensions. Specific drivers showing horrible driving violation (HDV) and dangerous driving violation (DDV) mainly endanger personal security and environmental damage. The identification of the focused drivers would contribute to formulate more refined management policies in carbon emission reduction, improve natural resource utilization, raise human capital benefit, and promote social sustainable development.

Linking nuclear energy, human development and carbon emission in BRICS region: Do external debt and financial globalization protect the environment?

Nuclear energy has the potential to play an influential role in energy transition efforts than is now anticipated by many countries. Realizing sustainable human development and reducing global climate crises will become more difficult without significantly increasing nuclear power. This paper aims to probe the role of nuclear energy, external debt, and financial globalization in sustaining human development and environmental conditions simultaneously in BRICS (Brazil, Russia, India, China, and South Africa) countries. This study applied a battery of second-generation estimation approaches over the period from 1990 to 2019. These methods are useful and robust to cross-countries dependencies, slope heterogeneity, parameters endogeneity, and serial correlation that are ignored in conventional approaches to generate more comprehensive and reliable estimates. The empirical findings indicate that nuclear energy and financial globalization contribute to human development, whereas external debt inhibits it. Similarly, financial globalization accelerates ecological deterioration, but nuclear energy and external debt promote environmental sustainability. Moreover, the study reveals bidirectional feedback causalities between human development, carbon emissions and nuclear energy consumption. The study offers useful policy guidance on accomplishing sustainable and inclusive development in BRICS countries.

Integrating GEDI and Landsat: Spaceborne Lidar and Four Decades of Optical Imagery for the Analysis of Forest Disturbances and Biomass Changes in Italy.

Forests play a prominent role in the battle against climate change, as they absorb a relevant part of human carbon emissions. However, precisely because of climate change, forest disturbances are expected to increase and alter forests’ capacity to absorb carbon. In this context, forest monitoring using all available sources of information is crucial. We combined optical (Landsat) and photonic (GEDI) data to monitor four decades (1985–2019) of disturbances in Italian forests (11 Mha). Landsat data were confirmed as a relevant source of information for forest disturbance mapping, as forest harvestings in Tuscany were predicted with omission errors estimated between 29% (in 2012) and 65% (in 2001). GEDI was assessed using Airborne Laser Scanning (ALS) data available for about 6 Mha of Italian forests. A good correlation (r2 = 0.75) between Above Ground Biomass Density GEDI estimates (AGBD) and canopy height ALS estimates was reported. GEDI data provided complementary information to Landsat. The Landsat mission is capable of mapping disturbances, but not retrieving the three-dimensional structure of forests, while our results indicate that GEDI is capable of capturing forest biomass changes due to disturbances. GEDI acquires useful information not only for biomass trend quantification in disturbance regimes but also for forest disturbance discrimination and characterization, which is crucial to further understanding the effect of climate change on forest ecosystems.

Threats of climate change and land use patterns enhance the susceptibility of future floods in India

The main objective of this work is the future prediction of the floods in India due to climate and land change. Human activity and related carbon emissions are the primary cause of land use and climate change, which has a substantial impact on extreme weather conditions, such as floods. This study presents high-resolution flood susceptibility maps of different future periods (up to 2100) using a combination of remote sensing data and GIS modelling. To quantify the future flood susceptibility various flood causative factors, Global circulation model (GCM) rainfall and land use and land cover (LULC) data are envisaged. The present flood susceptibility model has been evaluated through receiver operating characteristic (ROC) curve, where area under curve (AUC) value shows the 91.57% accuracy of this flood susceptibility model and it can be used for future flood susceptibility modelling. Based on the projected LULC, rainfall and flood susceptibility, the results of the study indicating maximum monthly rainfall will increase by approximately 40–50 mm in 2100, while the conversion of natural vegetation to agricultural and built-up land is about 0.071 million sq. km. and the severe flood event area will increase by up to 122% (0.15 million sq. km) from now on.

Feasible Distributed Energy Supply Options for Household Energy Use in China from a Carbon Neutral Perspective.

This contribution firstly proposed the concept of annual average power generation hours and analyzed per capita energy consumption, carbon emission, and the human development index from a macro perspective. On this basis, we compared the average household electrical energy consumption of urban and rural residents based on the data from CGSS-2015 from a micro perspective. The results show the positive correlation between carbon emissions per capita and the human development index and China’s regional imbalance characteristics between household electricity consumption and renewable energy distribution. Therefore, the distributed energy supply system is proposed as an effective complement to centralized power generation systems and is the key to synergizing human development and carbon emissions in China. Moreover, we analyzed the characteristics of distributed energy supply systems in the context of existing energy supply systems, pointing out the need to fully use solar energy and natural gas. Finally, two types of typical distributed energy supply systems are proposed for satisfying the household energy requirements in remote or rural areas of western and the eastern or coastal areas of China, respectively. Two typical distributed energy systems integrate high-efficiency energy conversion, storage, and transfer devices such as electric heat pumps, photovoltaic thermal, heat and electricity storage, and fuel cells.

The twilight zone of the Earth’s carbon cycle

The twilight zone of the Earth’s carbon cycle The oceans’ mesopelagic layer or ‘Twilight Zone’ (200–1,000 m) acts as the gateway for long-term storage of atmospheric carbon in the deep ocean, mitigating global warming due to human carbon emissions. In the OPERA project, we will perform comprehensive model simulations, constrained by observations, to better understand and quantify mesopelagic carbon budgets (and their uncertainty) in the North Atlantic Ocean.

Prediction of direct carbon emissions of Chinese provinces using artificial neural networks.

Closely connected to human carbon emissions, global climate change is affecting regional economic and social development, natural ecological environment, food security, water supply, and many other social aspects. In a word, climate change has become a vital issue of general concern in the current society. In this study, the carbon emission data of Chinese provinces in 1999–2019 are collected and analyzed, so as to identify the carbon emission of direct consumption per 10,000 residents in each province (including each municipal city and autonomous region) and the entire nation based on population data. The Arc Geographic Information Science Engine (ArcGIS Engine) and C#.NET platform are employed to call the MATLAB neural network toolbox. A model is selected and embedded in the prediction system to develop the entire system. This study demonstrates that the carbon emissions per resident in Northern China are significantly higher than those in Southern China, with the rate of carbon emissions continuing to increase over time. Compared with other models, the Elman neural network has a higher carbon emission prediction accuracy, but with more minor errors. For instance, its accuracy and prediction performance are improved by 55.93% and 19.48%, respectively, compared with the Backpropagation Neural Network (BPNN). The prediction results show that China is expected to reach its peak carbon emission in around 2025–2030. The above results are acquired based on the concept of carbon emissions and neural network model theories, supported by GIS component technology and intelligent methods. The feasibility of BPNN, Radial Basis Function (RBF) and Elman neural network models for predicting residential carbon emissions is analyzed. This study also designs a comprehensive, integrated and extensible visual intelligent platform, which is easy to implement and stable in operation. The trend and characteristics of carbon emission changes from 2027 to 2032 are explored and predicted based on the data about direct carbon emissions of Chinese provincial residents from 1999 to 2019, purposed to provide a scientific basis for the control and planning of carbon emissions.

The Impact of Demographic Changes on CO2 Emission Profiles: Cases of East Asian Countries

The demographic changes of East Asian countries have accelerated in recent years. With consideration of the linkage between human behavior and carbon emissions, it is necessary to consider demographic characteristics for the CO2 emission projections of these countries. This study examines how changes in the demographic structure affect the emission projections of three East Asian countries (South Korea, China, and Japan) by comparing two different vintages of population projections. The study constructed a dynamic computable general equilibrium (CGE) model and applied the most up-to-date dataset of population prospects, GTAP 10, and the labor force participation rate. By comparing UN2010 and UN2019 projections, the study examined the impact of demographic changes on CO2 emission profiles of the three East Asian countries. The simulation result showed that GDP, which represents economic activity along with the population, is the direct channel of CO2 emission projections. Moreover, the scenario analysis suggested the population factor as one of the main drivers of the CO2 emission projection and a clear positive relationship between GDP and CO2 emissions, though CO2 emissions are generally inelastic in response to a GDP decrease in the three East Asian countries. The finding indirectly implies that not only the size of the population but also demographic composition should be considered to project CO2 emissions, as the labor participation rate is an important factor to determine the production function.

The Relationship between Human Well-Being and Carbon Emissions

Governments around the world are actively exploring strategies to reduce carbon emissions and mitigate and adapt to the impacts of climate change. In addition to technological progress, promoting a transformation of residents’ behaviors to a low carbon mode is also a solution. Many people are concerned about how to reduce carbon emissions while ensuring human well-being. Starting from the comparative analysis of two main theories of human well-being, this paper sorted out existing well-being measurement methods from the perspectives of “top-down” and “bottom-up” and further sorted out research on the relationship between human well-being and energy carbon emissions. While “top-down” research is conducive to the layout of macro policies, “bottom-up” research can better help to promote the transformation of society to a low carbon life by estimating the energy consumption and carbon emissions contained in human needs. Current research discusses human well-being, human needs, energy use and carbon emissions, respectively, but they are not systematically integrated. Furthermore, this paper proposes a framework combining these aspects to analyze the relationship between human well-being and carbon emissions. In addition, this paper suggests future research directions.

Assessment of Human-Related Driving Forces for Reduced Carbon Uptake Using Neighborhood Analysis and Geographically Weighted Regression: A Case Study in the Grassland of Inner Mongolia, China

The ever-rising concentration of atmospheric carbon is viewed as the primary cause for global warming. To discontinue this trend, it is of urgent importance to either cut down human carbon emissions or remove more carbon from the atmosphere. Grassland ecosystems occupy the largest part of the global land area but maintain a relatively low carbon sequestration flux. While numerous studies have confirmed the impacts on grassland vegetation growth from climate changes and human activities, little work has been done to understand the driving forces for a reduced carbon uptake (RCU)—a loss in vegetation carbon sequestration because of inappropriate grassland management. This work focused on assessing RCU in the grassland of Inner Mongolia and understanding the influential patterns of the selected variables (including grazing intensity, road network, population, and vegetation productivity) related to RCU. Neighborhood analysis was proposed to locate optimized grassland management practices from historical data and to map RCU. Ordinary least squares (OLS) and geographically weighted regression (GWR) models were applied to explore the driving forces for RCU. The results indicated that the human-related factors, including stock grazing intensity, population density, and road network were likely to present a spatially varied impact on RCU, which accounted for more than 1/4 of the total carbon sequestration.