Increase In Carbon Emissions Research Articles

Discriminatory Pricing Strategy for Sustainable Tourism in Theme Parks considering Visitors’ Price Fairness and Service Value Perceptions

With the increase in carbon emissions in the tourism industry, more tourism enterprises need to make sustained investments in clean energy and green technologies. From the perspective of theme park revenue and operational management, such investments mainly come from admission fees and in-park consumption. The objective of this study is to discuss the role of discriminatory pricing strategies in supporting sustainable tourism in theme parks. Based on transaction utility theory and equity theory, visitors’ price fairness perception and service value perception are incorporated into the visitor utility function. On this basis, a goal-programming-based discriminatory pricing model with three goals is proposed: achieving the established revenue target, achieving distributed justice between visitors with unequal status (advantaged visitors and disadvantaged visitors), and achieving distributed justice between visitors and theme parks. The research results show that, for one thing, the proposed discriminatory pricing model can enable theme parks to secure sufficient funds to invest in low-carbon activities (Goal 1) while for another, visitors’ satisfaction, brand loyalty, and willingness to revisit and sustainably consume in theme parks are improved by the realization of distributed justice through the discriminatory pricing strategy (Goals 2 and 3).

Sustainable Process Study on Emergy and Carbon Emission Analysis of Building System Based on Neural Network Algorithm

Sustainable building systems can effectively reduce environmental pressures and mitigate the deterioration of the global climate. The sustainability of complex building systems is influenced by various factors. This article quantitatively analyzes building systems from an ecological emergy and carbon emissions perspective, and considers typical feedback structures’ impact. A neural network algorithm is employed for sustainability prediction analysis. The results demonstrate that both from an emergy and carbon emissions perspective, the operational phase of the building and the production phase of building materials are the main contributors (accounting for over 90%). Among the three types of feedback subsystems, the cross-feedback structure has a more significant impact and yields the best corrective effect. For example, the correction proportion of the building’s emergy sustainability parameter reaches 11.3%, while it is 15.8% for carbon emissions. The neural network model predicts a decreasing trend in the energy sustainability of buildings and increasing carbon emissions over time. To improve the sustainability of building systems, measures such as ecological landscape design and carbon sequestration in building materials are considered, which can enhance the sustainability of buildings to a certain extent.

Research on the influencing factors and prevention measures of long-term forest fire risk in Northeast China

Forest fires damage ecological environment and increase carbon emissions. To reduce the adverse effects of fire on forests, the long-term (2002–2022) fires data were analyzed in the Great Xing'an Mountains region of Northeast China. The important influencing factors of forest fires were revealed by analyzing different combustibles, meteorological, climatic factors, and the spatial and temporal distribution characteristics of forest fires. The results showed that Ledum palustre, Pinus silvestris L., and Rhododendron dauricum L. were the main tree species with high probability of forest fires according to the flammability difference of combustibles. The young and middle-aged forests accounted for more than 60% of the total forests, which were high flammability. Based on the regression analysis of meteorological data, temperature and precipitation had significant effects on forest fires. Affected by the lightning strikes, the total number of fires in 2017–2022 was high, and the high frequency of fires was in June and August. Because the fires were influenced by factors such as surface vegetation combustibles, precipitation, temperature, and ground slope, the north and southwest slopes had the highest fire incidence rates. In addition to the resource utilization of dead branches and leaves, planting tree species with high fire resistance is an important measure. Artificial precipitations are taken in an extremely dry environment. The management of old trees are strengthened in the western region of Great Xing'an Mountains. The types of fires that cause negative impacts on forests are the focus of monitoring and prevention. This study is of great significance in reducing forest fire risk, improving forest carbon sequestration function, increasing wood resources, and improving the public climate environment.

Delineation of Urban Development Boundary and Carbon Emission Effects in Xuzhou City, China

Urban development boundary (UDB) has always served as a crucial aspect of urban sprawl research. The objective of this paper is to investigate boundary delineation and carbon emission effects. Firstly, we examined the patterns of land use changes. Additionally, this paper utilized the FLUS model and land use carbon emission calculation model to delineate UDB and calculate carbon emission effects within UDB under typical scenarios. The research results are as follows: (1) Xuzhou city witnessed a significant increase in both forest land and construction land from 2010 to 2020. (2) Under the CPS, the area of cultivated land increased by 217.05 km2 compared to the NDS. The UDB area under the NDS and CPS was 971.50 km2 and 968.99 km2, respectively. (3) Compared to the NDS, the CPS led to a net carbon emission increase of 4759.93 t within the UDB. Therefore, we should enhance the carbon sequestration and emission reduction capacity of the agricultural system. This study is beneficial for expanding the depth of research on the UDB and guiding the low-carbon urban development. We sincerely encourage readers to download this paper to improve this paper.

Synergistic effects of heat and carbon on sustainable urban development: Case study of the Wuhan Urban Agglomeration

The rapid pace of urbanization has resulted in pressing climatic challenges, notably an escalation in carbon emissions and rising urban temperatures. Urban expansion frequently coincides with heightened carbon emissions, releasing substantial quantities of greenhouse gases such as CO2. This exacerbates the urban heat island (UHI) effect and poses a threat to urban living conditions. To shed light on the intricate relationship between land surface temperature (LST) and carbon emissions and provide novel insights into the synergistic regulation of “heat” and “carbon” in sustainable urban development, we conducted an in-depth investigation of the Wuhan Urban Agglomeration spanning from 2003 to 2018. Employing data on impervious surface fraction (ISF), LST, and carbon emissions, we harnessed weighted center and standard deviation ellipse analyses to explore the spatiotemporal expansion patterns of ISF, LST, and carbon emissions. We examined the relationship between LST and carbon emissions using spatial correlation analysis. The results revealed that regions with high carbon emissions and elevated LST were predominantly concentrated in the central urban areas of each city, aligned with the distribution of high-density impervious surfaces. The weighted centers of ISF, LST, and carbon emissions exhibited a progressive shift toward the central region of Wuhan, intensifying spatial clustering effects. The global Moran's I index for LST and carbon emissions increased from 0.33 in 2003 to 0.48 in 2018, signifying a strong positive correlation between LST and carbon emissions. In the clustering maps of LST and carbon emissions, the high-high clustering trend persistently expanded, signifying a shift from “dispersion” to “agglomeration” in urban heat and carbon effects. In regions marked by consistent carbon emission growth, the correlation coefficient (R) between carbon emissions and LST was 0.4, and the Pearson coefficient was 0.64, both higher than those in areas with gradual carbon emission growth. Within stable carbon emission growth areas, every 0.1-ton increase in carbon emissions has a 40% probability of a 1.33 °C temperature rise. As the rate of carbon emissions increased, the positive synergistic effect between carbon emissions and LST continued to increase. This study provides empirical support and academic groundwork to reduce urban emissions and mitigate the effects of UHIs.

Driving impact and spatial effect of the digital economy development on carbon emissions in typical cities: a case study of Zhejiang, China.

The digital economy (DE) not only drives economic innovation and development but also has significant environmental effects by promoting lower carbon emissions. To investigate the spatial effects of DE on urban carbon emissions, this study comprehensively measures the level of DE development based on the panel data from 11 typical cities in Zhejiang Province from 2011 to 2020, by comparing analysis using different regression models. The following conclusions are obtained: (1) The total carbon emissions (TC) of Zhejiang cities in general show a fluctuating change trend of first increasing and then slowly decreasing, while carbon emission intensity and carbon emission per capita in general show a fluctuating change trend of decreasing. Cities with high TC are primarily concentrated in the Hangzhou Bay city cluster, accounted for 62 ~ 65% of the province's carbon emissions. The development of the DE in Zhejiang cities shows steady growth, but there are large differences among cities, with Hangzhou and Ningbo standing out as particularly prominent. (2) There is a significant inverted U-shaped relationship between the DE and the level of carbon emissions in Zhejiang Province. The influence coefficient of the DE on the primary term of TC is 0.613, and the influence coefficient of the quadratic term of TC is - 1.008. (3) In terms of the spatial spillover effect of the DE on carbon emissions, the study finds that compared to the direct effect, the spatial spillover effect is not significant. However, the allocation of transport resources shows a positive spatial spillover effect (increasing carbon emissions, coefficient value is 0.138), while technological progress shows a somewhat negative spatial spillover effect (decreasing carbon emissions, coefficient value is - 0.035). (4) The study also finds that the smart city pilot policy significantly reduces urban carbon emissions. Moreover, the effect of the DE on carbon emissions is confirmed through the significance test of the quadratic term when replacing the geographical and economic distance weight matrices. This indicates that the empirical findings are robust to these tests. Finally, several countermeasures to reduce carbon emissions are proposed from the perspective of DE development.

An Integrated Interval Type-2 Fuzzy Set Model for Evaluating Circular Low Carbon Suppliers in a Developing Country

Increasing carbon emissions and environmental degradation have led to rapid depletion of resources. This requires circular low carbon (CLC) supply chains which extend the lifecycle of resources as much as possible. The aim of this study is to propose an integrated model for CLC supplier selection. First, economic, environmental and social responsibilities related to carbon management are identified. Then, DANP (Dematel-Analytic Network Process) and COPRAS (Complex Proportional Assessment) model with interval type-2 (IT-2) fuzzy sets are applied to evaluate the suppliers of a case energy company in Turkey. In this study, the ranking of dimensions from the most important to the least yielded the following order: environmental, social, and economic. In turn, carbon policy, carbon emission risks and low carbon supplier collaboration are found to be the most important environmental criteria. The current study has been used for the first time for a CLC supplier selection problem. Further, the study innovatively employs IT-2 fuzzy sets to better address differences and uncertainties that may come into play with a group of experts’ linguistic judgments. The validity of the proposed method is determined through sensitivity and comparative analysis.

Assessment of the environmental kuznets curve within EU-27: Steps toward environmental sustainability (1990–2019)

Reducing environmental pollution is a critical goal in global environmental economics and economic development. The European Union (EU) faces environmental challenges due to its development activities. Here we present a comprehensive approach to assess the impact of carbon dioxide (CO2) emissions, energy consumption (EC), population structure (POP), economy (GDP), and policies on the environment within the EU using the environmental Kuznets curve (EKC). Our research reveals that between 1990 and 2019, the EU-27 experienced an increase of +1.18 million tonnes of oil equivalent (Mtoe) per year in energy consumption (p < 0.05), while CO2 emissions decreased by 24.25 million tonnes (Mt) per year (p < 0.05). The highest reduction in CO2 emissions occurred in Germany (−7.52 Mt CO2 annually), and the lowest in Latvia (−0.087 Mt CO2 annually). The empirical EKC analysis shows an inverted-U shaped relationship between GDP and CO2 emissions in the EU-27. Specifically, a 1% increase in GDP results in a 0.705% increase in carbon emission, while a 1% increase in GDP2 leads to a 0.062% reduction in environmental pollution in the long run (p < 0.01). These findings indicate that economic development within the EU has reached a stage where economic growth positively impacts the environment. Overall, this study provides insights into the effectiveness of environmental policies in mitigating degradation and promoting green growth in the EU 27 countries.

Low-pressure steam generating heat pump – A design and field implementation case study

Steam is commonly used in industries for cooking in their kitchens, and diesel or LPG-fired boilers are often used to generate it. The use of a high-temperature heat pump to generate steam can help reduce emissions and lower the CO2 footprint by eliminating these fired sources. The following study details the design and consistent operation of a steam-generating heat pump (SGHP) system that employs a high-temperature scroll compressor and functions on the principles of a vapour compression system. SGHP generates steam with a stagnation pressure of 1.5 bar, a flow rate of 81.43 kg/hr, and a temperature of 108.52 ℃. The system was simulated using DWSIM under steady-state conditions with no pressure drop, and steam was generated through a flash tank. SGHP was also installed in a canteen that previously used a diesel boiler, and data from 31 days of operation was analyzed to evaluate system performance, environmental impact, and overall savings. The system COP was 2.2. The analysis showed that an average daily energy savings of 57.79 kWh and an average cost savings of Rs. 1125.46 were achieved. SGHP (with high temperature circuit only) increased carbon emissions by 20.3 % compared to diesel, based on India's emission factor of 790 g CO2/kWh. However, considering the emission factor of the European Union, the steam-generating heat pump has the potential to reduce carbon emissions by 57.8 %.

Effect of Scale and Structure Changes of China’s High-Carbon Industries on Regional Carbon Emissions

China’s carbon emissions have a stable industrial concentration. In recent years, the carbon emissions of the six major high-carbon industries have accounted for approximately 80% of the national total and are thus priority areas for emission reduction. With the promotion of energy-saving and emission-reduction policies, the structure and scale of high-carbon industries in various regions have undergone changes, but their carbon reduction effects show significant regional differences. Based on China’s provincial panel data from 2006 to 2020, this study discusses the structural characteristics of high-carbon industries with their proportion of energy-based industries and measures their scale characteristics with their output values. On this basis, a fixed-effects model is used to analyze the single and synergistic effects of the scale and structure of high-carbon industries on carbon emissions in each province. The results indicate that changes in the scale and structure of high-carbon industries significantly affect carbon emissions but show regional differences in both the single and synergistic effects. When considering these synergistic effects, the single effect of high-carbon industries on carbon emissions will be weakened. In regions with large-scale high-carbon industries, the increase in the proportion of energy-based industries significantly increases carbon emissions, but this effect gradually weakens as the overall scale expands. In areas with small-scale high-carbon industries, the increase in the proportion of energy-based industries has a relatively small effect on carbon emission growth that gradually increases with the overall scale. In addition, the implementation of the carbon emission trading policy has a significant moderating effect on the carbon emissions of high-carbon industries and strongly promotes its reduction.

Fine allocation of sectoral carbon emissions at block scale and contribution of functional zones

The rapid development of cities has resulted in a substantial increase in carbon emissions, which has a detrimental impact on the environment. Previous studies had focused on large-scale carbon emissions; however, the carbon emissions from urban functional zones that consist of blocks as basic units have not been explored. In this study, we used Open Street Map (OSM) road and point-of-interest (POI) data to classify Shanghai into different functional zones and investigated their contributions to carbon emissions from various sectors. A top–down method was used to finely allocate the statistical data to different functional zones, and a random forest algorithm was used to investigate the contributions of different features to the regional carbon emissions. Among the 5677 units examined in this study, the majority belonged to residential zones, followed by commercial and industrial zones. The carbon emission contributions of the three zones were 26, 34, and 12%, respectively. With respect to sectors, higher carbon emissions originated from the residential and tertiary sectors, primarily concentrated in the urban centers and western city areas. Secondary and primary sectors followed suit, with the highest concentrations being from urban peripheries. This study can be used to optimize and rationalize the allocation of functional areas; notably, our study presents a novel direction for assigning carbon emissions responsibilities to different urban functions. Furthermore, the method presented in this study can be customized for carrying out analysis for other countries, with the aim of estimating and addressing carbon emissions on a sectoral basis.

Spatial and temporal characteristics of coal consumption and carbon emissions in China.

Based on the requirement of energy restructuring in China's "Dual Carbon" target, this study measures the carbon emissions of 30 provinces (autonomous region and municipality directly under the central government) in China from 1997 to 2019, the relationship between coal consumption and carbon emissions in China was investigated by using the methods of robust test, panel cointegration test, and Granger causality test; exploratory spatial data analysis (ESDA) is applied to analyze the spatial characteristics of energy consumption and carbon emissions in China. The results show that (i) there is a long-term two-way causal relationship between coal consumption and carbon emissions in China; (ii) both coal consumption and carbon emissions in China show spatial correlation, with obvious locational characteristics, and are relatively stable in the spatial pattern as a whole, with relatively small changes in the short term; (iii) both coal consumption and carbon emissions show significant positive correlation and positive spatial correlation, with an increase in coal consumption and an increase in carbon emissions. The local Moran's I can show that there are fewer areas that differ from the overall trend, with H-H agglomeration maintaining a stable contiguous trend, L-L agglomeration decreasing, and contiguous characteristics gradually disappearing.

Production, Consumption, Export and Carbon Emission for Coal Commodities: Cases of Indonesia and Australia

The contribution of this study is to analyze the impact of a series of economic activities from the coal commodity on carbon emission. This research specifically focuses on coal exporting countries in Asia Pacific, including Indonesia and Australia during the period 2000 to 2022. The purpose of this study is to investigate the influence between production, consumption and export of coal and its determinants, and also the effect on carbon emission. This study applies the simultaneous equation approach and panel regression. The main findings of this study are that coal consumption, coal export, technological innovation and coal price have a positive and significant effect on coal production. Furthermore, coal production, domestic income and domestic investment have a positive and significant effect on coal consumption, while coal export has a negative and significant effect. Then, coal production and coal stock have a positive and significant effect on coal export, while coal consumption and domestic investment have a negative and significant effect. Finally, production, consumption and export of coal commodities contribute to increasing carbon emission. We recommend the coal exporting countries analyzed in this study to implement coal energy control in addressing increasing carbon emission at the global level.

The Influence of Environmental Performance, Company Size, and Independent Commissioners on Carbon Emission Disclosure

Global warming caused by the increase in carbon emissions is the impact of the company's operational activities that use machines with large capacities. The study aims to empirically prove the influence of environmental performance, firm size and independent board of commissioners on carbon emission disclosure. The population in the study were 165 companies in the energy sector and basic materials sector companies listed on the Indonesian Stock Exchange during the study period (2018-2021). Determination in this sample used purposive sampling and based on existing criteria obtained as many as 29 companies with a total of 116 data observation. Data analysis techniques use panel data regression with EViews software version 9. The results of the study show that simultaneously environmental performance, firm size and independent board of commissioners have a significant impact on carbon emission disclosure. Partially variable environmental performance and firm size have a significant influence on carbon emission disclosure. Independent board of commissioners partially had no significant influence on carbon emission disclosure.

Creation of Environmentally Friendly Super "Dinitrotoluene Scavenger" Plants.

Pervasive environmental contamination due to the uncontrolled dispersal of 2,4-dinitrotoluene (2,4-DNT) represents a substantial global health risk, demanding urgent intervention for the removal of this detrimental compound from affected sites and the promotion of ecological restoration. Conventional methodologies, however, are energy-intensive, susceptible to secondary pollution, and may inadvertently increase carbon emissions. In this study, a 2,4-DNT degradation module is designed, assembled, and validated in rice plants. Consequently, the modified rice plants acquire the ability to counteract the phytotoxicity of 2,4-DNT. The most significant finding of this study is that these modified rice plants can completely degrade 2,4-DNT into innocuous substances and subsequently introduce them into the tricarboxylic acid cycle. Further, research reveals that the modified rice plants enable the rapid phytoremediation of 2,4-DNT-contaminated soil. This innovative, eco-friendly phytoremediation approach for dinitrotoluene-contaminated soil and water demonstrates significant potential across diverse regions, substantially contributing to carbon neutrality and sustainable development objectives by repurposing carbon and energy from organic contaminants.

Energy consumption characteristics and rooftop photovoltaic potential assessment of elevated metro station

The current trend of urbanization has spurred the growth of the urban railway transportation sector, leading to increased carbon emission and environmental challenges. Applying renewable energy assisted power supply systems into metro stations is a feasible method for achieving decarbonization in urban transit systems. This study comprehensively reveals the real energy profile of a metro station on an hourly scale and establishes a multi-objective model to investigate the energy flexibility of the metro station with integrated battery energy storage for on-site renewable electricity consumption maximization. The results show that the elevated station has a photovoltaic potential of 20 %–25 %, in terms of self-sufficiency rate, when the rooftop of the station is fully utilized for photovoltaic array installation. Moreover, a photovoltaic-battery system in addition to a multi-objective mixed inter linear programming model is presented for the circumstance, there existing idle land around the station, to achieve a near-zero carbon station. It is indicated that the near-zero carbon station can be achieved with a minimum payback period of 11.5 years by optimizing the photovoltaic array size and battery storage capacity, leading to the annual carbon emission reduced from 457.1 to 91.4 tons. It is anticipated this study could provide reference for clean electrification and decarbonization in urban metro system.

Enhanced sequestration of CO2 from simulated electrolytic aluminum flue gas by modified red mud

The aluminum industry is facing severe economic and environmental problems due to increasing carbon emissions and growing stockpiles of red mud (RM). RM is a strongly alkaline, high-emission solid waste from the alumina industry with potential for CO2 sequestration. However, the effectiveness of RM carbon sequestration is poor, and the mechanism behind it is not well understood. In this study, the effect of microwave and tube furnace activation of RM on CO2 sequestration in alumina was first investigated at different temperatures. The result showed that the CO2 sequestration capacity of unmodified RM (URM) was only 14.35 mg/g at ambient temperature and pressure, and the CO2 sequestration capacity could be increased to 52.89 mg/g after high-temperature activation and modification. Besides, high-temperature activation and modification will effectively improve the carbon sequestration capacity of RM. The carbonized RM was characterized by FT-IR, SEM, XRD, laser particle size, TG-DSC, and pH measurements. In addition, the mechanism of RM capturing CO2 was also proposed, which shows that CO2 was finally sequestered in the RM as CaCO3. The change in particle size distribution and the mineral phase in the RM indicated that high-temperature activation modification positively affects the application of RM to the sequestration of CO2. This study can provide a promising technology for the low-carbon and green development of the aluminum industry, as well as achieving the waste treatment and utilization objective.

Aging and carbon emissions in Asian economies: Policy recommendation from panel quantile regression

Climate change due to global greenhouse gas emissions poses a severe threat, and energy sectors contribute about two‐thirds of the emissions. However, energy plays an important role in the well‐being of the aging population, which is increasing in Asian countries. Therefore, this study aims to discern how the male and female populations of different age groups affect carbon emissions in Asian economies. The findings from the panel quantile regression reveal that economic growth escalates emissions across all quantiles along with energy use. The female population in the 0–14 years age group reduces emissions at all quantiles, while the male population of the same age group enhances the emissions. However, the male and female population between 15 and 64 years increases carbon emissions across all Asian economies. Furthermore, the male population above 65 years is responsible for low emissions, while the female counterparts are responsible for high emissions. Finally, we offer a set of policy recommendations based on our findings. For instance, there is a need to educate the male population between 0 and 14 years on environmental degradation to encourage an environment‐friendly lifestyle. Also, policies should aim to persuade the 15–64 age group to use renewables, which can help mitigate CO2 emissions.

Spatio-Temporal Characteristics of Industrial Carbon Emission Efficiency and Their Impacts from Digital Economy at Chinese Prefecture-Level Cities

In the pursuit of China’s dual carbon goals, identifying spatio-temporal changes in industrial carbon emission efficiency and their influencing factors in cities at different stages of development is the key to effective formulation of countermeasures to promote the low-carbon transformation of Chinese national industry and achieve high-quality economic development. In this study, we used balanced panel data of 270 Chinese cities from 2005 to 2020 as a research object: (1) to show spatio-temporal evolution patterns in urban industrial carbon emission efficiency; (2) to analyze the aggregation characteristics of industrial carbon emission efficiency in Chinese cities using Global Moran’s I statistics; and (3) to use the hierarchical regression model for panel data to assess the non-linear impact of the digital economy on the industrial carbon emission efficiency of cities. The results show the following: (1) the industrial carbon emission efficiency of Chinese cities exhibited an upward trend from 2005 to 2020, with a spatial distribution pattern of high in the south and low in the north; (2) China’s urban industrial carbon emission efficiency is characterized by significant spatial autocorrelation, with increasing and stabilizing correlation, and a relatively fixed pattern of spatial agglomeration; (3) there is a significant inverted-U-shaped relationship between the digital economy and the industrial carbon emission efficiency of cities. The digital economy increases carbon emissions and inhibits industrial carbon emission efficiency in the early stages of development but inhibits carbon emissions and promotes industrial carbon emission efficiency in mature developmental stages. Therefore, cities at all levels should reduce pollution and carbon emissions from high-energy-consuming and high-polluting enterprises, gradually reduce carbon-intensive industries, and accelerate the digital transformation and upgrading of enterprises. Western, central, and eastern regions especially should seek to promote the sharing of innovation resources, strengthen exchanges and interactions relating to scientific and technological innovation, and jointly explore coordinated development routes for the digital economy.

Prolonged war reverses carbon emissions from an early decline to a late increase – Evidence from Syria

Given that war can have a serious impact on the climate, this article is aimed to discuss the impact of warfare on carbon emissions by examining changes in CO2 before and during the war in Syria based on the kaya constant equation and the LMDI decomposition method. In the decade before the war, population was the largest contributor, making up 32.64% of the total 51.02% increase in carbon emissions. The only factor that offsetting carbon emissions was energy intensity, making a 22.30% curbing effect. In the early stage of the war, carbon emissions decreased by 56.38%, in which per capita GDP contributed 37.55% of the total CO2 decline. Carbon intensive of energy was the only factor promoting the carbon increase with a 4.67% contribution. In the late war, carbon emissions start to resume slow increase with energy intensity and economy turning negative to positive. It can be speculated that the impact of the war on CO2 emissions: (i) in the first years of the war, CO2 would drop significantly at the cost of significant population decline and economic recession, the least desirable and the worst way to reduce carbon emissions. (ii) if evolves into a prolonged war, it would reverse carbon emissions from decline to increase, although the population and the economy are both falling. This research, therefore contends that once war is triggered, there is no other solution to prevent this worst-case scenario of Population Decline - Economic Recession - Increased Carbon Emissions from happening, unless the war is stopped immediately.