Emission Trends Research Articles

Dry season assessment of carbon storage and emission from upland and riparian soils in the Ganga River basin

Soil organic carbon (C) and nitrogen (N) are profoundly affected by changes in land use and land cover (LULC), especially by farming in riparian zones. The five LULC classes were selected in contiguous order, i.e., undisturbed forest (CALref), conventional agriculture lands (CAL), reservoir riparian zones (RSRZ), river riparian zones (RRZ), and undisturbed riparian zones (RZref) in the Ganga River basin to study the C storage and emission trends at a landscape scale. The riparian soils showed higher moisture, temperature, and bulk density than the upland soils, strongly determining the spatial variations in the CO2 equivalent (CO2e), C:N ratio, and CO2 efflux. Riparian CO2e was more bulk density-dependent, while upland CO2e showed greater dependence on TOC. The C:N ratio showed a higher mean value in the reference soils (CALref and RZref) than in the other soils (P < 0.05). The total nitrogen (TN), total protein (TP), and NH4-N (ammonium‑nitrogen) showed the following trend: RSRZ > CAL > RRZ > RZref > CALref. The stepwise multiple regression models illustrated that soil moisture was the primary regulator of the C:N ratio and CO2 efflux in the upland soils while the temperature in the riparian soils. These intrinsic soil variables resulted in 1.15 to 2.26 times higher CO2 efflux from the cultivated soils (CAL, RSRZ, and RRZ) than from the reference soils. Hence, the present study revealed how agricultural practices tangibly increase the riparian system's carbon footprint while offering unsustainable livelihood options to farmers.

Feasibility of peak temperature targets in light of institutional constraints

Despite faster-than-expected progress in clean energy technology deployment, global annual CO2 emissions have increased from 2020 to 2023. The feasibility of limiting warming to 1.5 °C is therefore questioned. Here we present a model intercomparison study that accounts for emissions trends until 2023 and compares cost-effective scenarios to alternative scenarios with institutional, geophysical and technological feasibility constraints and enablers informed by previous literature. Our results show that the most ambitious mitigation trajectories with updated climate information still manage to limit peak warming to below 1.6 °C (‘low overshoot’) with around 50% likelihood. However, feasibility constraints, especially in the institutional dimension, decrease this maximum likelihood considerably to 5–45%. Accelerated energy demand transformation can reduce costs for staying below 2 °C but have only a limited impact on further increasing the likelihood of limiting warming to 1.6 °C. Our study helps to establish a new benchmark of mitigation scenarios that goes beyond the dominant cost-effective scenario design.

Electrochemical CO2 Reduction in the Presence of Flue Gas Impurities: Effect of NOx, SOx and O2

Electrochemically converting CO2 to valuable chemical products using renewable energy sources may present solutions to alter current increasing CO2 emission trends. Currently, electrolyzer research is almost exclusively done with pure CO2 streams that - in practice - would originate from carbon capture units that prevent CO2 release to the atmosphere [1]. The capture and purification steps are cost intensive processes which is why direct usage of flue gases that contain impurities is a highly interesting approach. For this reason, the performance of CO2 electrolyzers equipped with state-of-the-art Bi2O3 or Ag catalysts is investigated in this work with an impure CO2 feed stream containing SO2, NO or O2. The short-term effect of highly concentrated SO2 (10 000 ppm) and NO (8 300 ppm) impurities during CO2 electrolysis was previously described [2,3]. Our work [4] elaborates further on this matter through 20 h experiments with flue gas-like concentrations of 200 ppm SO2 or NO. The results show a stable operation without severe FE losses (remains >90%) and structural adaptations to neither Ag nor Bi2O3 by these impurities. The presence of oxygen in flue gas streams at concentrations of several percent is common and causes a major decrease in FE to the target products (CO or HCOO-) during electrolysis. This is a result of the competition between the ORR and CO2RR with the former occurring more preferentially. To overcome this, we have discovered two possible strategies, which allow high FE’s to CO2RR products even in the presence of oxygen. (1) By operating the electrolyzer at a sufficiently high overall current density, all of the O2 entering and reaching the electrode surface gets reduced, allowing the CO2RR to become the dominant reaction. Although this approach has a higher electricity penalty, the extra cost is negligible compared to the cost otherwise associated with the CO2 purification for CO2 streams with an O2 content below 3%. (2) Given that the ORR is mostly occurring at the carbon-based gas diffusion layers another approach to improve CO2RR efficiency in the presence of oxygen is to replace this conventional support with ultra hydrophobic PTFE membrane filters. Since they are inefficient oxygen reduction catalysts, CO2RR again takes up the major fraction of the applied current density. A downside of using PTFE is that an additional conductive layer is required in case the catalyst itself (e.g. for bismuth oxide) is not conductive enough. These results thus not only offer new insights into the impact of gaseous impurities during CO2 electrolysis, but also suggest a strategy to improve electrolyzer performance with O2-containing feed streams. Bibliography [1] Lee M.Y., Environ. Sci. and Technol.; DOI: 10.1080/10643389.2019.1631991[2] Luc W.; JACS, DOI: 10.1021/jacs.9b03215[3] Ko B., Nature Comm. DOI : 10.1038/s41467-020-19731-8[4] Van Daele S ., J. Appl. Catal. B, Environ.; DOI: 10.1016/J.APCATB.2023.123345 Figure 1

Modeling carbon dioxide emissions reduction

The paper proposes a new scientific approach to modeling carbon dioxide (CO2) emissions in individual countries, based on the tools of Kohonen self-organizing maps and the provisions of economic theory. This work is based on conducted a comprehensive analysis of wide set of indicators influencing carbon dioxide emissions and decarbonization processes in 40 selected countries for 10 years time span from 2013 till 2022. During clustering these nations using self-organizing maps, 14 key indicators were chosen that cover economic and demographic growth, energy consumption, CO2 emission data, and includes trade of energy recourses for wider analysis and identification of countries with similar decarbonization potential, economic development and trade energy recourses possibilities. This approach exposed distinct clusters of nations with varying decarbonization capabilities and tracked the progress of analyzed countries in terms of CO2 emission changes in dynamic.The analysis of clustering was focused on identification leaders and followers in decarbonization activities. The leaders cluster includes Sweden, Norway, New Zealand and other countries exhibited leadership in developing a climate-resilient economy among 40 countries. Hence, all countries positioned closer to the leader cluster on the map demonstrated higher efficiency in their decarbonization pathways. Consequently, these properties of Kohonen maps provide a foundation for formulating general recommendations to achieve an efficient and effective low-carbon economy for followers including Ukraine, Kazakhstan and other energy intensive countries.Distinct countries decarbonization profiles resulting from clustering were used to develop practical recommendations towards estimating and establishing targeted CO2 emission levels for analyzed countries, conducting continued promoting renewable energy utilization, enhancing cross-border energy trade, diversifying energy portfolios, etc. Also, the proposed methodology can be utilized to project future emissions trends for each country based on cluster-specific models and facilitate policy decisions making process related to mitigating carbon emissions.

Research Progress on Production and Emission of CH4 and N2O from Urban Rivers

Methane （CH4） and nitrous oxide （N2O） are concerning greenhouse gases. Urban rivers have been important emission sources of CH4 and N2O in recent years. It is meaningful for city greenhouse gas reduction to provide a systematic analysis of spatiotemporal characteristics, mechanisms, and influencing factors of the production and emission of CH4 and N2O from urban rivers. This study combed measured data of urban river CH4 and N2O dissolution concentrations and emission fluxes from related literature published in the past 20 years and also concluded the spatiotemporal characteristics of urban river CH4 and N2O emissions. This study estimated that CH4 and N2O emissions （expressed by CO2-eq） from urban rivers in Beijing were 234.63 and 59.53 Gg CO2-eq in 2018, whereas CH4 and N2O emissions （expressed by CO2-eq） from urban rivers in Shanghai were 159.86 and 260.24 Gg CO2-eq in 2018, respectively. These results demonstrated that urban rivers have become important CH4 and N2O emission sources. This study summarized the production/consumption processes and import/export pathways of CH4 and N2O in urban rivers. What is more, this study discussed the main influencing factors of urban river CH4 and N2O production and emissions from the perspectives of river environmental conditions and urbanization effects. At last, the present work prospected the future research trends of urban river CH4 and N2O emissions and provides urban rivers with scientific support for greenhouse gas reduction.

Sustainability Measures of the Apparel Industry: A Longitudinal Analysis of Apparel Corporations’ Sustainability Efforts

Climate change is a paramount challenge of our era, with profound implications for various sectors, including the apparel industry, known for its extensive environmental footprint throughout the entire clothing lifecycle. This study assesses the industry’s carbon emission trends using six years of data (2017–2022) focusing on Scope 1 and Scope 2. Employing a one-way repeated measures ANOVA, this study evaluates the impact of sustainability strategies on emissions. The results indicate a notable decrease in Scope 2 and overall carbon emissions, while Scope 1 emissions remain relatively unchanged despite a downward trend. These findings underscore the success of current sustainability measures in reducing emissions, especially in Scope 2, and highlight the need for ongoing and intensified efforts in Scope 1 areas. This research offers valuable insights into the apparel industry’s environmental impact, emphasizing the critical role of continued strategic actions in addressing climate change.

Accurate and rapid reactive flow simulations using dynamic load balancing and sparse analytical Jacobian approach

The present study introduces a rapid and accurate customized solver on the OpenFOAM platform for large-scale industrial computations. Specifically, a sparse analytical Jacobian approach utilizing the SpeedCHEM library was implemented to enhance the efficiency of the ordinary differential equation solver. The dynamic load balancing code was used to distribute computational workloads uniformly across multiple processes. Optimization continued with open multi-processing to improve parallel computing efficiency and the local time stepping scheme to maximize individual cell time steps. The effectiveness and robustness of the customized solver were first validated using Sandia flames D–F as benchmarks. The results showed that the customized solver exhibited better strong scaling characteristics and led to a speed increase of up to 30 times for two-dimensional Sandia flame D calculations. The numerical predictions for temperature and species distribution closely matched the experimental trends, confirming the accuracy of the solver. Subsequently, a three-dimensional numerical study on a 10 kW ammonia co-combustion furnace was conducted, exploring the performance of the solver in large-scale reactive simulations. Results analysis indicated that the acceleration capability was reduced due to increased communication overhead between processors, achieving up to 7.06 times speed-up. However, as the size of the reaction mechanism increases, better acceleration capabilities can be demonstrated. The numerical predictions could closely replicate experimental trends, effectively predicting NO emission trends within the combustion furnace. This study offers one viable solution for rapid and accurate calculations in the OpenFOAM platform, which could be applied in the subsequent ammonia industrial combustion processes.

Projected global sulfur deposition with climate intervention

Even with immediate implementation of global policies to mitigate carbon dioxide emissions, the impacts of climate change will continue to worsen over the next decades. One potential response is stratospheric aerosol injection (SAI), where sulfur dioxide is released into the stratosphere to block incoming solar radiation. SAI does not reduce the level of carbon dioxide in the atmosphere, but it can slow warming and act as a stopgap measure to give the world more time to pursue effective carbon reduction strategies. While SAI is controversial, it remains a technically feasible proposition. It ought to be thoroughly modeled both to characterize global risks better and to further the scientific community’s understanding of stratospheric aerosol dynamics. SAI relies on sulfate aerosols which have a lifetime of several years in the stratosphere but will eventually be deposited back onto Earth’s surface. While sulfate is an important nutrient for many ecosystems, high concentrations can cause acidification, eutrophication, and biodiversity loss. We use model outputs from the Geoengineering Model Intercomparison Project (GeoMIP) to track the impacts of sulfur deposition from SAI to various ecoregions through comparison with historical climate and future Shared Socioeconomic Pathway (SSP) scenarios. Our results demonstrate that dry sulfur deposition will continue to decline worldwide, regardless of scenario, from a high of 41 Tg S/yr in 1981 to under 20 Tg S/yr by 2100. Wet sulfur deposition, however, is much more uncertain and further work needs to be done in this area to harmonize model estimates. Under SAI, many ecoregions will experience notably different sulfur deposition regimes by the end of the century compared to historical trends. In some places, this will not be substantially different than the impacts of climate change under SSP2–4.5 or SSP5–8.5. However, in some ecoregions the model projections disagree dramatically on the magnitude of future trends in both emissions and deposition, with, for example, UKESM1–0-LL projecting that SO42- deposition in deciduous needleleaf forests under G6 Sulfur will reach 394 % of SSP2–4.5 deposition by the 2080 s while CESM2-WACCM projects that SO42- deposition will remain at 170 % of SSP2–4.5 deposition during that same time period. Our work emphasizes the lack of agreement between models and the importance of improving our understanding of SAI impacts for future climate decision-making.

Study on the establishment of air pollutant and carbon emission inventory and collaborative emission reduction potential of China's coking industry from 2012 to 2022

Coking industry is usually regarded as a high pollution and high energy consumption industry. China is accelerating its efforts to reduce pollution and carbon emissions in the industrial sector, which has received little attention as the world's largest producer of coke. Therefore, in this study, the trend of air pollution and carbon emissions in China's coking industry and the path of coordinated emission reduction were studied. The results indicate that the average annual emissions of PM, SO2, NOx, VOCs, and CO2 in China's coking industry from 2012 to 2022 amount to 205.98, 69.47, 193.45, 599.80 Gg and 191.10 Tg, respectively. The main sources of PM, SO2, NOx, VOCs and CO2 in coking industry were coal preparation (51.5 %), charge and pushing (39.5 %), coke oven gas (99.8 %), byproduct recovery (47.0 %) and fuel combustion (87.5 %). The emissions from coking plants in central and southern Shanxi, eastern and southern Hebei, and central Shandong are the most concentrated. Ultra-low emission transformation and deep treatment of VOCs have greatly reduced pollutant emissions in key areas of air pollutant control, but the actual emission reduction effect of these measures has been weakened by the additional emissions caused by the increase of coke production in other non-key areas. The research on synergetic emission reduction path shows that there is a great synergistic benefit between air pollutants and CO2 emission reduction in coking industry. It is estimated that the APeq (air pollutants and carbon equivalent) of China's coking industry in 2025, 2028 and 2030 will decrease by 38.2 %, 63.5 % and 70.8 % respectively compared with 2022. With the continuous promotion of pollution reduction and carbon reduction measures, the emission reduction potential of China's coking industry will gradually shift from key areas to non-key areas.

Emission Control in Expressway Systems: Vehicle Emission Inventory and Policy Scenario Analysis

Expressway systems play a vital role in facilitating intercity travels for both passengers and freights, which are also a significant source of vehicle emissions within the transportation sector. This study investigates vehicle emissions from expressway systems using the COPERT model to develop multi-year emission inventories for different pollutants, covering the past and future trends from 2005 to 2030. Thereinto, an integrated SARIMA-SVR method is designed to portray the temporal variation of vehicle population, and the possible future trends of expressway vehicle emissions are predicted through policy scenario analysis. The Jiang–Zhe–Hu Region of China is taken as the case study to analyze emission control in expressway systems. The results indicate that (1) carbon monoxide (CO) and volatile organic compounds (VOCs) present a general upward trend primarily originating from passenger vehicles, while nitrogen oxides (NOx) and inhalable particles (PM) display a slowing upward trend with fluctuations mainly sourcing from freight vehicles; (2) vehicle population constraint is an effective emission control policy, but upgrading the medium- and long-haul transportation structure is necessary to meet the continuous growth of intercity trips. Expressway vehicle emission reduction effectiveness can be further enhanced by curtailing the update frequency of emission standards, along with the scrapping of high-emission vehicles.

Simulation of China’s Carbon Peak Path Based on Random Forest and Sparrow Search Algorithm—Long Short-Term Memory

How to decouple economic growth from carbon dioxide emissions and achieve low-carbon transformation of the Chinese economy has become an urgent problem that needs to be solved. Firstly, the Tapio index is used to identify China’s carbon peak status, and then the Technology Choice Index (TCI) and economic complexity are introduced into the comprehensive factor analysis framework for carbon dioxide emissions. Key influencing factors are identified using random forest and ridge regression. On this basis, a novel sparrow search algorithm–long short-term memory (SSA-LSTM) model which has more prediction accuracy compared with past studies is constructed to predict the dynamic evolution trend of carbon dioxide emissions, and in combination with scenario analysis, the path towards the carbon peak is simulated. The following conclusions are obtained: The benchmark scenario peaks in 2031, with a peak of 12.346 billion tons, and the low-carbon scenario peaks in 2030, with a peak of 11.962 billion tons. The extensive scenario peaks in 2037, with a peak of 13.291 billion tons. Under six scenarios, it can be concluded that energy intensity is the key factor in reducing the peak. These research results provide theoretical support for decision-makers to formulate emission reduction policies and adjust the carbon peak path.

Decomposition of carbon emissions and companies’ willingness-to-pay for Green Fund: evidence from the manufacturing sector in Busan, South Korea

ABSTRACT The impact of carbon neutrality on the manufacturing sector, through regulatory and carbon pricing mechanisms, pushes companies toward environmental-friendly management. This paper seeks to examine the manufacturing sector’s willingness-to-pay (WTP) for the Busan Green Fund, an initiative designed to incentivize companies to adopt green management practices in Busan Metropolitan City. Three industries with the highest level of CO2 emissions are selected utilizing the Logarithmic Mean Divisia Index (LMDI) method. Companies in these three key industries are surveyed regarding their WTP, beyond their energy costs, for the Busan Green Fund. The study also investigates the characteristics of these companies. The average WTP is an 11.1% premium on energy costs. The regression results show that WTP is significantly influenced by the level of investment in energy-saving technology and company size. Based on our analysis of CO2 emission trends within the manufacturing sector, policy implications are suggested for sustainable development via the operation of a dedicated fund.

Can China avoid the energy trilemma in achieving carbon peak?: A dynamic scenario forecasting study based on energy transition

Harmonizing the cleanliness and economics of energy is an effective means of integrating economic growth with carbon peaking goals, and realizing sustainable development in China. However, due to the existence of the energy trilemma, a worthwhile research question is how to fulfill the carbon peaking commitment while guaranteeing China's stable national development. Based on this, this paper not only discusses the nonlinear relationship between clean energy consumption transition and economic growth through threshold regression, but also simulates several scenarios of energy transition dynamics with different growth rates by using Oracle Crystal Ball software to forecast the future trend of China's carbon emissions up to 2030, aiming to find a transition scenario that can make the peak of carbon emissions appear without significant economic burden, so as to put forward countermeasures and suggestions to avoid falling into the energy trilemma in the process of fulfilling the commitment of carbon peaking. This paper finds that: firstly, there is an inverted U-shaped non-linear relationship between clean energy consumption transition and economic growth, with the inflection point of the inverted U-shape being 0.595; and secondly, only when energy transition exceeds 0.535 in 2030, the peak of carbon emissions (about 12,922.28 Mt) will occur. In summary, this paper argues that China should steadily advance energy transition, avoiding either too fast or too slow a pace (bring clean energy consumption transition to within the 0.535–0.595 range by 2030), in order to secure economic growth while honoring its commitment to peak carbon. Therefore, China should prudently design a coal exit strategy to avoid falling into the energy trilemma when promoting energy transition. Moreover, if China wants to further fulfill its commitment to carbon neutrality by 2060, it needs to continue to deepen the construction of green financial markets in order to better balance the relationship between energy cleanliness and economy.

Controlling carbon emissions through modeling and optimization: addressing an earth system and environment challenge

This study aims to analyze the trend of carbon dioxide CO2 emissions from various sources in Pakistan between 1990 and 2020 to effectively model the underlying dynamics of carbon emissions. The design and fitting of the model to historical emissions data reveal significant trends and patterns, highlighting the alarming increase in CO2 emissions. These findings underscore the necessity for robust policy interventions to mitigate emissions and achieve sustainable development goals (SDGs). This work can contribute to addressing the challenges of recent plans targeting global warming and the climate emergency. By controlling these parameters, the mean reversion can be managed, allowing for the control of the increasing rate of carbon emissions in regions threatened by climate change. The O-U model provides a valuable framework for understanding the stochastic nature of carbon emissions, offering insights into the persistence and variability of emission levels over time. The optimized parametric thresholds of the stochastic model, after synchronizing it with the real data, reveal that the challenge of carbon emissions cannot be naturally resolved and serious interventions are highly desired. These include robust policy measures to mitigate emissions, improve air quality, and combat climate change.

High temperature sensitivity of Arctic isoprene emissions explained by sedges

It has been widely reported that isoprene emissions from the Arctic ecosystem have a strong temperature response. Here we identify sedges (Carex spp. and Eriophorum spp.) as key contributors to this high sensitivity using plant chamber experiments. We observe that sedges exhibit a markedly stronger temperature response compared to that of other isoprene emitters and predictions by the widely accepted isoprene emission model, the Model of Emissions of Gases and Aerosols from Nature (MEGAN). MEGAN is able to reproduce eddy-covariance flux observations at three high-latitude sites by integrating our findings. Furthermore, the omission of the strong temperature responses of Arctic isoprene emitters causes a 20% underestimation of isoprene emissions for the high-latitude regions of the Northern Hemisphere during 2000-2009 in the Community Land Model with the MEGAN scheme. We also find that the existing model had underestimated the long-term trend of isoprene emissions from 1960 to 2009 by 55% for the high-latitude regions.

Continuous wildfires threaten public and ecosystem health under climate change across continents

Wildfires burn approximately 3%–4% of the global land area annually, resulting in massive emissions of greenhouse gases and air pollutants. Over the past two decades, there has been a declining trend in both global burned area and wildfire emissions. This trend is largely attributed to a decrease in wildfire activity in Africa, which accounts for a substantial portion of the total burned area and emissions. However, the northern high-latitude regions of Asia and North America have witnessed substantial interannual variability in wildfire activity, with several severe events occurring in recent years. Climate plays a pivotal role in influencing wildfire activity and has led to more wildfires in high-latitude regions. These wildfires pose significant threats to climate, ecosystems, and human health. Given recent changes in wildfire patterns and their impacts, it is critical to understand the contributors of wildfires, focus on deteriorating high-latitude areas, and address health risks in poorly managed areas to mitigate wildfire effects.

NOx Emission Trend Prediction for the Waste Incineration Process Based on Partial Least Squares with the Time Series Reconstruction and Exponential Weighting

AbstractAccurate prediction of nitrogen oxide (NOx) emission is crucial for effectively controlling pollution in municipal solid waste incineration processes. However, it is challenging to construct a NOx emission prediction model with high prediction accuracy and easy engineering application. To address this, this paper proposes a robust and easily applicable NOx emission trend prediction model oriented to engineering applications, utilizing the partial least squares (PLS) method with the time series reconstruction and exponential weighting (TS‐EW‐PLS). The model is verified using operational data from an actual waste incineration process, and comparative analysis with the PLS model showed that the TS‐EW‐PLS model achieved a remarkable improvement of 27–38 % in prediction performance.

Annual progress in global carbon capture, utilization, and storage in 2023

AbstractSince the industrial revolution, global anthropogenic CO2 emissions have surged dramatically to unsustainable levels, resulting in severe issues, such as global warming, extreme weather events, and species extinction. In response to this critical situation, extensive efforts have been undertaken across academia, industry, and policymaking sectors to deploy carbon capture, utilization, and storage (CCUS) technologies. Here, we present the annual summary of global CCUS for the year 2023. We begin by discussing the trends of anthropogenic CO2 emissions and atmospheric CO2 concentrations, and then offer an up‐to‐date summary of progress in academia, industry, and policy, respectively. In academia, we analyze the number and categories of publications and highlight some key breakthroughs. In the industry sector, we meticulously collect and present information on operational commercial carbon‐capture and storage facilities. Furthermore, we elucidate significant policy announcements and reforms across diverse regions. This concise and comprehensive annual report aims to inspire ongoing efforts and collaboration among academia, industry, and policymakers toward advancing carbon neutrality.

Exploring the dynamics and trends of carbon emission spatiotemporal patterns in the Chengdu–Chongqing Economic Zone, China, from 2000 to 2020

Analysis of the spatial–temporal pattern and trend of carbon emissions provides an important scientific basis for the development of a low-carbon economy. Based on the corrected NPP-VIIRS and DMSP/OLS nighttime light data, a carbon emission model for the Chengdu–Chongqing Economic Zone (CCEZ) in China is constructed. Furthermore, the article establishes an integrated qualitative and quantitative research system. The qualitative results show that at the city and county scales, the high carbon emission areas and counties are mainly distributed in Chengdu and Chongqing, while the low carbon emission areas are concentrated in the marginal cities of the CCEZ and the counties with low levels of industrialization around the Sichuan Basin. The high-carbon emission zone tended to expand to the north, and the low-carbon emission zone tended to expand to the south. At the grid scale, the carbon emissions of the CCEZ fluctuated and increased from 2000 to 2020, forming a trend connected with those of the central city, with high carbon emissions at the core and radiating outward expansion. Quantitative analysis revealed that carbon emissions at the county and grid scales exhibited a significant positive global spatial correlation, and the overall correlation degree exhibited an increasing trend.

Research on the Development of Urban Industrialization and Ecological Environment: A Case Study of Tangshan and Detroit

Taking Tangshan and Detroit as examples, this paper explores the strategies for the coordinated development of industrialization and ecological environment in Tangshan. The relationship between industrialization and ecological environment in Tangshan is investigated by studying the industrialization process and urbanization indicators. The Environmental Kuznets Curve is employed to analyze the correlation with wastes in Tangshan and Detroit. The results indicate that the traditional inverted "U" curve cannot adequately capture the relationship between industrialization and ecological environment in Tangshan. Tangshan and Detroit exhibit different trends in air emissions, waste water discharge and solid particulate matter emissions, with variations in goodness of fit and F-value tests. These differences may be influenced by various factors such as the characteristics of the cities. In conclusion, studying the coordinated development of industrialization and ecological environment in Tangshan contributes to a deeper understanding of environmental issues in the industrialization process and explores the challenges and opportunities in urban economic transformation. By comparing the experiences of Tangshan and Detroit, Tangshan can identify effective pathways to address issues such as industrial upgrading and promote sustainable urban development and economic growth.