How Much Human-Caused Global Warming Should We Expect with Business-As-Usual (BAU) Climate Policies? A Semi-Empirical Assessment

Global Warming and Anthropogenic Emissions of Water Vapor

The two major components of greenhouse gases, CO2 and water, are indispensable for sustaining life on Earth. Water vapor is the most significant greenhouse gas that has provided the earth with an "atmospheric blanket" and prevented the surface of the earth from freezing. However, contemporary climate models largely consider the influence of water vapor as a factor within positive feedback loops, while the possibility of direct anthropogenic emissions of water vapor as primary drivers of global warming remains underexplored. In particular, a common assumption has been that the global atmospheric water vapor will increase by about 6 to 7% in response to each 1 °C of warming caused by the nonaqueous greenhouse gases in accordance with the Clausius-Clapeyron equation, and this increased moisture content will lead to an increased greenhouse gas effect. However, the Clausius-Clapeyron equation is based on two-phase equilibrium, and there is no a priori physical basis that it can be applied to the earth's climate for which the water vapor does not always coexist with a condensed phase. Here, we utilized global specific humidity data from the NCEP/NCAR reanalysis data set to examine whether the Clausius-Clapeyron equation can form a basis for such positive feedback commonly assumed in the contemporary climate models. Our results show (1) qualitiatively, the linear nature of the Clausius-Clapeyron equation demonstrates a significant level of consistency when averaged over expansive regions like specific latitudes around the globe, (2) this consistency does not extend to individual locations where a plot of (ln Pv) vs (1/T) becomes nonlinear, indicating substantial undersaturation that varies with time, (3) quantitatively, the discrepancies between the observed and the expected values of the slopes are wide-ranging, and (4) the absolute amount of water vapor increased substantially above the population centers and the agricultural areas in the Northern Hemisphere between 1960 and 2020. Human activities appear to have substantial impacts on the local water vapor content in the atmosphere. Once we assume that anthropogenic emissions of water vapor are the source of local water vapor content in the atmosphere, it can, together with the air circulation patterns (Hadler, Ferrel and polar), provide an explanation for the observations that Arctic ice has been melting at a much more accelerated rate than Antarctic ice.

Can the new energy vehicles (NEVs) and power battery industry help China to meet the carbon neutrality goal before 2060?

China is working to boost the manufacture, market share, sales, and use of NEVs to replace fuel vehicles in transportation sector to get carbon reduction target by 2060. In this research, using Simapro life cycle assessment software and Eco-invent database, the market share, carbon footprint, and life cycle analysis of fuel vehicles, NEVs, and batteries were calculated from the last five years to next 25 years, with a focus on the sustainable development. Results indicate globally, China had 293.98 m vehicles and 45.22% worldwide highest market share, followed by Germany with 224.97 m and 42.22% shares. Annually China's NEVs production rate is 50%, and sales account for 35%, while the carbon footprint will account for 5.2 E+07 to 4.89 E+07 kgCO2e by 2021-2035. The power battery production 219.7 GWh reaches 150%-163.4%, whereas carbon footprint values in production and use stage of 1 kWh of LFP 44.0 kgCO₂eq, NCM-146.8 kgCO₂eq, and NCA-370 kgCO₂eq. The single carbon footprint of LFP is smallest at about 5.52 E+09, while NCM is highest at 1.84 E+10. Thus, using NEVs, and LFP batteries will reduce carbon emissions by 56.33%-103.14% and 56.33% or 0.64 Gt to 0.006 Gt by 2060. LCA analysis of NEVs and batteries at manufacturing and using stages quantified the environmental impact ranked from highest to lowest as ADP > AP > GWP > EP > POCP > ODP. ADP(e) and ADP(f) at manufacturing stage account for 14.7%, while other components account for 83.3% during the use stage. Conclusive findings are higher sales and use of NEVs, LFP, and reduction in coal-fired power generation from 70.92% to 50%, and increase in renewable energy sources in electricity generation expectedly will reduce carbon footprint by 31% and environmental impact on acid rain, ozone depletion, and photochemical smog. Finally, to achieve carbon neutrality in China, the NEVs industry must be supported by incentive policies, financial aid, technological improvements, and research and development. This would improve NEV's supply, demand, and environmental impact.

The Resilience of Vegetation to the 2009/2010 Extreme Drought in Southwest China

The 2009/2010 extreme drought in southwest China (SWC) was a “once-in-a-century” drought event, which caused unprecedented damage to the regional ecology and socioeconomic development. The event provided a chance to explore the resilience of vegetation growth and productivity to the extreme drought. Here, we used the self-calibrating Palmer drought severity index (scPDSI) to describe the characteristics of the extreme drought. Vegetation growth and productivity indices, including the normalized difference vegetation index (NDVI), leaf area index (LAI), and gross primary productivity (GPP), were applied to analyze the resilience of different vegetation types to the extreme drought. Our results showed that the extreme drought event occurred mainly in Yunnan Province, Guizhou Province, central and northern Guangxi Zhuang Autonomous Region, and northwestern Sichuan Province. The spatial heterogeneity of the extreme drought was related to the temperature increase and water deficit. During the extreme drought, the vegetation growth and productivity of evergreen broadleaf forest were the least suppressed, whereas cropland was greatly suppressed. The recovery of cropland was higher than that of evergreen broadleaf forest. NDVI and LAI were recovered in more than 80% of the drought-affected area within 5 months, whereas GPP required a longer time to recover. Moreover, the results of multiple linear regression showed that an increase in surface soil moisture was able to significantly improve the resistance of vegetation NDVI and LAI in evergreen broadleaf forest, evergreen needleleaf forest, evergreen broadleaf shrubland, deciduous broadleaf shrubland, and grassland. Our study highlights the differences in the resilience of different vegetation types to extreme drought and indicates that surface soil moisture is an important factor affecting vegetation resistance in SWC.

Damage Functions and the Social Cost of Carbon: Addressing Uncertainty in Estimating the Economic Consequences of Mitigating Climate Change

Mitigating the effects of human-induced climate change requires the reduction of greenhouse gases. Policymakers must balance the need for mitigation with the need to sustain and develop the economy. To make informed decisions regarding mitigation strategies, policymakers rely on estimates of the social cost of carbon (SCC), which represents the marginal damage from increased emissions; the SCC must be greater than the marginal abatement cost for mitigation to be economically desirable. To determine the SCC, damage functions translate projections of carbon and temperature into economic losses. We examine the impact that four damage functions commonly employed in the literature have on the SCC. Rather than using an economic growth model, we convert the CO₂ pathways from the Representative Concentration Pathways (RCPs) into temperature projections using a three-layer, energy balance model and subsequently estimate damages under each RCP using the damage functions. We estimate marginal damages for 2020-2100, finding significant variability in SCC estimates between damage functions. Despite the uncertainty in choosing a specific damage function, comparing the SCC estimates to estimates of marginal abatement costs from the Shared Socioeconomic Pathways (SSPs) indicates that reducing emissions beyond RCP6.0 is economically beneficial under all scenarios. Reducing emissions beyond RCP4.5 is also likely to be economically desirable under certain damage functions and SSP scenarios. However, future work must resolve the uncertainty surrounding the form of damage function and the SSP estimates of marginal abatement costs to better estimate the economic impacts of climate change and the benefits of mitigating it.

Monitoring migratory birds of India's largest shallow saline Ramsar site (Sambhar Lake) using geospatial data for wetland restoration

Globally, saline lakes occupy about 23% by area, and 44% by volume. Importantly, these lakes might desiccate by 2025 due to agricultural diversion, illegal encroachment, or modify due to pollution, and invasive species. India's largest saline lake, Sambhar is currently shrinking at a phenomenal rate of 4.23% every decade due to illegal saltpan encroachments. This study aims to identify the trend of migratory birds and monthly wetland status. Birds' survey was conducted for 2019, 2020 and 2021, and combined it with literature data of 1994, 2003, and 2013, for understanding their visiting trends, feeding habits, migratory and resident birds ratio, along with ecological diversity index analysis. Normalized Difference Water Index (NDWI) was scripted in Google Earth Engine. Results state that lake has been suitable for 97 species. Highest NDWI values was 0.71 in 2021 and lowest 0.008 in 2019. Notably, the decreasing trend of migratory birds coupled with decreasing water level indicates the dubious status for its existence. If these causal factors are not checked, it might completely desiccate. Authors recommend a few steps that might help conservation. Least, the cost of restoration might exceed the revenue generation.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11273-022-09875-3.

Effects of COVID-19 on mobility GHG emissions: Case of the city of Lahti, Finland

The coronavirus disease COVID-19 has spread worldwide since early 2020, and it has impacted mobility emissions due to mobility restrictions and e.g. increased remote work. This creates a good opportunity to assess how mobility emissions have reduced due to COVID-19. This research is based on data related to mobility distances and modes that have been automatically collected by using a mobile phone application in the city of Lahti, Finland. The results show that mobility decreased in total by approximately 40% during the first wave of COVID-19 in spring 2020. The global warming potential decreased at the same time by approximately 36%. In addition, a considerable shift in modal shares could be seen. The relative modal share of passenger cars increased by 6 percentage points while the share of public transport decreased by 18 percentage points. Despite the considerable reduction, further reductions in emissions from mobility are needed to meet the 1.5 degree climate targets in the urban mobility sector. However, further reductions can be reached also by increasingly using renewable mobility energy sources.

Life Cycle Assessment of Dynamic Water Flow Glazing Envelopes: A Case Study with Real Test Facilities

High initial costs hinder innovative technologies for building envelopes. Life Cycle Assessment (LCA) should consider energy savings to show relevant economic benefits and potential to reduce energy consumption and CO2 emissions. Life Cycle Cost (LCC) and Life Cycle Energy (LCE) should focus on investment, operation, maintenance, dismantling, disposal, and/or recycling for the building. This study compares the LCC and LCE analysis of Water Flow Glazing (WFG) envelopes with traditional double and triple glazing facades. The assessment considers initial, operational, and disposal costs and energy consumption as well as different energy systems for heating and cooling. Real prototypes have been built in two different locations to record real-world data of yearly operational energy. WFG systems consistently showed a higher initial investment than traditional glazing. The final Life Cycle Cost analysis demonstrates that WFG systems are better over the operation phase only when it is compared with a traditional double-glazing. However, a Life Cycle Energy assessment over 50 years concluded that energy savings between 36% and 66% and CO2 emissions reduction between 30% and 70% could be achieved.

Ensemble Prediction Approach Based on Learning to Statistical Model for Efficient Building Energy Consumption Management

With the development of modern power systems (smart grid), energy consumption prediction becomes an essential aspect of resource planning and operations. In the last few decades, industrial and commercial buildings have thoroughly been investigated for consumption patterns. However, due to the unavailability of data, the residential buildings could not get much attention. During the last few years, many solutions have been devised for predicting electric consumption; however, it remains a challenging task due to the dynamic nature of residential consumption patterns. Therefore, a more robust solution is required to improve the model performance and achieve a better prediction accuracy. This paper presents an ensemble approach based on learning to a statistical model to predict the short-term energy consumption of a multifamily residential building. Our proposed approach utilizes Long Short-Term Memory (LSTM) and Kalman Filter (KF) to build an ensemble prediction model to predict short term energy demands of multifamily residential buildings. The proposed approach uses real energy data acquired from the multifamily residential building, South Korea. Different statistical measures are used, such as mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and R2 score, to evaluate the performance of the proposed approach and compare it with existing models. The experimental results reveal that the proposed approach predicts accurately and outperforms the existing models. Furthermore, a comparative analysis is performed to evaluate and compare the proposed model with conventional machine learning models. The experimental results show the effectiveness and significance of the proposed approach compared to existing energy prediction models. The proposed approach will support energy management to effectively plan and manage the energy supply and demands of multifamily residential buildings.

Atmospheric Temperature and CO2: Hen-Or-Egg Causality?

It is common knowledge that increasing CO2 concentration plays a major role in enhancement of the greenhouse effect and contributes to global warming. The purpose of this study is to complement the conventional and established theory, that increased CO2 concentration due to human emissions causes an increase in temperature, by considering the reverse causality. Since increased temperature causes an increase in CO2 concentration, the relationship of atmospheric CO2 and temperature may qualify as belonging to the category of “hen-or-egg” problems, where it is not always clear which of two interrelated events is the cause and which the effect. We examine the relationship of global temperature and atmospheric carbon dioxide concentration in monthly time steps, covering the time interval 1980–2019 during which reliable instrumental measurements are available. While both causality directions exist, the results of our study support the hypothesis that the dominant direction is T → CO2. Changes in CO2 follow changes in T by about six months on a monthly scale, or about one year on an annual scale. We attempt to interpret this mechanism by involving biochemical reactions as at higher temperatures, soil respiration and, hence, CO2 emissions, are increasing.

Atmospheric Temperature and CO2: Hen-or-Egg Causality?

It is common knowledge that increasing CO2 concentration plays a major role in enhancement of the greenhouse effect and contributes to global warming. The purpose of this study is to complement the conventional and established theory that increased CO2 concentration due to human emissions causes an increase of temperature, by considering the reverse causality. Since increased temperature causes an increase in CO2 concentration, the relationship of atmospheric CO2 and temperature may qualify as belonging to the category of “hen-or-egg” problems, where it is not always clear which of two interrelated events is the cause and which the effect. We examine the relationship of global temperature and atmospheric carbon dioxide concentration at the monthly time step, covering the time interval 1980–2019, in which reliable instrumental measurements are available. While both causality directions exist, the results of our study support the hypothesis that the dominant direction is T → CO2. Changes in CO2 follow changes in T by about six months on a monthly scale, or about one year on an annual scale. We attempt to interpret this mechanism by involving biochemical reactions, as at higher temperatures soil respiration, and hence CO2 emission, are increasing.

Energy and Climate Policy—An Evaluation of Global Climate Change Expenditure 2011–2018

Concern for climate change is one of the drivers of new, transitional energy policies oriented towards economic growth and energy security, along with reduced greenhouse gas (GHG) emissions and preservation of biodiversity. Since 2010, the Climate Policy Initiative (CPI) has been publishing annual Global Landscape of Climate Finance reports. According to these reports, US$3660 billion has been spent on global climate change projects over the period 2011–2018. Fifty-five percent of this expenditure has gone to wind and solar energy. According to world energy reports, the contribution of wind and solar to world energy consumption has increased from 0.5% to 3% over this period. Meanwhile, coal, oil, and gas continue to supply 85% of the world’s energy consumption, with hydroelectricity and nuclear providing most of the remainder. With this in mind, we consider the potential engineering challenges and environmental and socioeconomic impacts of the main energy sources (old and new). We find that the literature raises many concerns about the engineering feasibility as well as environmental impacts of wind and solar. However, none of the current or proposed energy sources is a “panacea”. Rather, each technology has pros and cons, and policy-makers should be aware of the cons as well as the pros when making energy policy decisions. We urge policy-makers to identify which priorities are most important to them, and which priorities they are prepared to compromise on.

Machine Learning Based Hybrid System for Imputation and Efficient Energy Demand Forecasting

The ongoing upsurge of deep learning and artificial intelligence methodologies manifest incredible accomplishment in a broad scope of assessing issues in different industries, including the energy sector. In this article, we have presented a hybrid energy forecasting model based on machine learning techniques. It is based on the three machine learning algorithms: extreme gradient boosting, categorical boosting, and random forest method. Usually, machine learning algorithms focus on fine-tuning the hyperparameters, but our proposed hybrid algorithm focuses on the preprocessing using feature engineering to improve forecasting. We also focus on the way to impute a significant data gap and its effect on predicting. The forecasting exactness of the proposed model is evaluated using the regression score, and it depicts that the proposed model, with an R-squared of 0.9212, is more accurate than existing models. For the testing purpose of the proposed energy consumption forecasting model, we have used the actual dataset of South Korea’s hourly energy consumption. The proposed model can be used for any other dataset as well. This research result will provide a scientific premise for the strategy modification of energy supply and demand.