This research explores the dynamic relationships between ecological footprint, economic performance, financial development, energy usage, and foreign direct investment (FDI) in South Asian economies utilizing the panel data from 1971 to 2018. In panel data analysis, conventional methods generally ignore the issues of cross-sectional dependency and the heterogenous nature of cross-sectional units. The other concern with the existing research is that most of the studies ignore the significance of ecological footprint while evaluating financial development and FDI as sources of environmental changes. The long-term relationship among the indicators is tested utilizing the Westerlund cointegration test. The findings support both environmental Kuznets curve and pollution haven hypotheses for South Asian economies. Besides, the empirical findings suggest that financial development increases environmental conservation while energy usage substantially disrupts the environment of the selected south Asian nations. Additionally, the heterogeneous causality analysis reveals the causal relationships between the variables. Thus, overall results recommend that the South Asian economies need to boost economic growth without compromising the environment, decrease fossil fuel usage, enhance financial sector growth and incentivize environmentally friendly FDI to conserve the environment in the region.
Aggregate demand or supply at equilibrium is commonly used as a representative of the macroeconomic activity of an economy whereby aggregate demand denotes the behaviour of individuals and households. However, aggregate demand can also directly affect environmental deterioration via changes in aggregate production. This study tried to explore this relationship, known as the demand-based Environmental Kuznets Curve (Demand EKC) and the role of different knowledge economy indicators. Knowledge economy indicators are proposed to influence consumption patterns, altering the demand EKC that empirical studies have understudied. For this purpose, secondary data for 147 countries were collected from 2008 to 2018, also classified as development-wise. This study found that aggregate demand significantly affects carbon emissions. The long-run results are estimated using the Fully Modified Ordinary Least Square method. Controlling factors like renewable energy consumption, population density, and financial development significantly affect carbon emissions in sample countries. This study has incorporated four pillars of a knowledge-based economy and the results showed that these indicators helped reduce consumption-related CO2 emissions.
Green power conversion is the shift away from traditional fuels towards clean energy sources such as nuclear power plants, hydroelectric dams, wind farms, and solar panels. This research examines the impact of clean energy demand and green financing on reducing carbon emissions in 29 economies in Europe and Asia from 2007 to 2020. The study used a two-step differenced GMM estimator for the available data set spanning 2007 to 2020. The study found that rising demand for nuclear power helps to achieve a carbon-neutral agenda, but insufficient funding for renewable energy leads to higher carbon emissions. The research suggests increasing investment in nuclear energy and green financing can improve regional environmental quality. The study found a causal link between fuel imports, nuclear power and regional growth. It also determined that fuel imports, chemical use, green financing and the need for nuclear energy will likely impact regional environmental quality. The research recommends allocating more resources toward innovation to boost energy efficiency and expanding investment in renewable and nuclear energy production industries via green finance. The study also highlights the need to encourage the development of renewable energy sources to cut carbon emissions and establish a sustainable society.
The challenge of achieving sustainable economic development with a secure environmental system is a global challenge faced by both developed and developing countries. Energy Efficiency (EE) is crucial in achieving sustainable economic growth while reducing ecological impacts. This research utilizes the Slack-Based Measure Data Envelopment Analysis (SBM-DEA) and the Malmquist-Luenberger Index (MLI) method to evaluate EE and productivity changes from 1995 to 2020 across G20 countries. The study uses four different input–output bundles to gauge the impact of renewable and non-renewable energy consumption and carbon emissions on EE and productivity changes. The study results show that including renewable energy consumption improves the average EE from 0.783 to 0.8578, but energy productivity declines from 1.0064 to 0.9988. Incorporating bad output (carbon emissions) in the estimation process enhances renewable EE and productivity change, resulting in an average EE of 0.6678 and MLI of 1.0044. Technological change is identified as the primary determinant of energy productivity growth in scenarios 1 and 2, while technical efficiency determines energy productivity change in scenarios 3 and 4. The Kruskal-Wallis test reveals a significant statistical difference between the mean EE and MLI scores of G20 countries.
Renewable energy, energy efficiency, and nuclear energy research and development (RER, EER, and NER) budgets are immensely important to fulfill sustainable development goals 7, 9, and 13, by accelerating energy innovation, energy transition, and climate control. The literature on the drivers of the load capacity factor (LCF), a recently developed ecological quality measure, is mounting; however, the roles of energy investments in the LCF are largely unknown. Accordingly, this study assesses the impacts of RER, EER, NER, and financial globalization (FIG) on the LCF using data from 1974 to 2018 for Germany. Advanced and reliable time series tests (Augmented ARDL, DOLS, and Fourier causality) are adopted to analyze cointegration, long-run impacts, and causal connections. The outcomes unveil that both green energy and energy efficiency R&D promote the LCF by enhancing ecological quality. However, the positive impact of NER on the LCF is found to be weaker than the impacts of RER and EER. FIG curbs ecological degradation by expanding the LCF. Additionally, the U-shaped connection between economic growth (ECG) and the LCF confirms the load capacity curve. Therefore, policymakers should focus on RER and EER to preserve the environment and promote sustainable growth.
Digital financial inclusion (DFI) has the advantage of promoting information sharing, reducing transaction costs, and providing microloan platforms for small and medium-sized enterprises. It has also made outstanding contributions to decreasing CO2 emissions. However, the volatility correlation between DFI and CO2 emissions is still relatively unexplored. This research uses the spatial autoregressive process with conditional heteroscedastic errors (SARspARCH) model to evaluate the spatial fluctuation spillover impacts of DFI on CO2 emissions in 284 Chinese cities covering the period 2011–2016 following the IPAT model. The results indicate that CO2 emissions have significant spatial spillover and volatility effects. The fitted value of SARspARCH estimation results is more realistic than the SAR and spARCH model. DFI alleviates average CO2 emissions in Chinese cities. Moreover, spatial volatility weakens the negative influence of DFI on average carbon emissions. This study provides insights from which governments can strengthen inter-regional communication and synergistic emission-reduction capabilities, and promote the digitization of the financial sector to achieve carbon neutrality goals.
The captivating surge of energy transitions in the major industrialized nations has elevated the global demand for critical minerals. The demand pattern has enabled mineral-abundant emerging economies like Indonesia to enter the international market by exporting mineral goods. Accordingly, we investigate the Indonesian mineral export supply's response to the renewable energy production of the 18 clean energy-generating countries, considering crude oil and mineral prices, exchange rates, and economic growth of the resource and importer countries from 1990 to 2020. In doing so, we apply the Poisson Pseudo-maximum Likelihood (PPML) approach to measuring the panel gravity model for mineral exports in Indonesia. As a result, we observe a significant response of Indonesia's mineral export supply to the renewable energy generation of the 18 mineral importing countries. Besides, mineral and crude oil prices are insignificant, whereas the importer countries' exchange rates and income growth positively influence Indonesia's mineral export growth. However, Indonesia's income factor negatively affects its mineral export supply. Finally, we validate our results using an alternative estimator, the Driscoll-Kraay robust standard error estimation technique. Therefore, our findings suggest implementing Indonesia's existing mineral policy to produce finished mineral goods to materialize the worldwide vision of energy transitions toward a crossroad of net-zero emissions by the middle of the current century.
There is growing attention from governments and regulators towards crucial matters such as climate change and global warming, resulting in a pressing need to investigate the factors that make it possible for businesses to engage in green finance (GF). The externality of environmental pollution prioritizes the need of green innovation (GI) in public management. GF distributes financial resources to the research and development (R&D) of clean energy and environmentally friendly goods and processes; it is complementary to the GI process for environmental protection. GF policies help to alleviate the impacts of financial constraints and GI impaired industries involving new products, processes, services and the global market. To better understand how GF and GI have functioned as a catalyst for circular economy practices, this paper seeks to present a historical and contemporary overview of these concepts. The research is thoroughly dissected by a systematic literature evaluation of articles from 2016 to 2023 that appear in peer-reviewed journals and are indexed in the SCOPUS database. To attain supply chain circularity, this article encompasses four major research themes concerning the adoption of GF and green technologies. The research also includes a network analysis of shortlisted articles to examine the overall citation trends. It is shown that several institutional theories are associated with the investigated area. As a final step, a framework is provided to illustrate how GF and GIs might be used to achieve supply chain circularity. The research findings provide a novel concept related to GF within the context of GI which are significant for environmentalists, policymakers, green investors, and researchers. Through its findings, the study provides a conceptual framework that promotes sustainable strategies to effectively balance financial considerations and environmental innovation. It helps to leverage the potential of green research and practice to create value for businesses and to benefit society at large. The analysis provides an unexplored and significant contribution to current literature in terms of delivering evidence of the past and present approaches to GF and GI in a circular economy. The results of this study will attract the attention of policymakers and stakeholders to develop and combine the two concepts in research and practice to attain environmental balance in the circular economy and to promote long term sustainability.
All economies are concerned about rising carbon emissions, which contribute to environmental degradation. The current paper formulates a novel framework to scrutinize the impacts of shocks in economic complexity, FDI, environmental technology, and renewable energy on carbon emission in the leading clean energy investment countries, spanning the period from 1995 to 2020. In spite of the constraint for better environmental defence and the realization of the Sustainable Development Goals (SDGs), this paper introduces an empirical approach utilizing the Panel NARDL methodology to investigate the asymmetrical connections between carbon emissions and relevant exogenous factors. Furthermore, we utilize additional techniques, namely AMG and CCEMG, to enhance the robustness of our findings. Our empirical findings reveal that positive shocks in economic complexity, FDI, environmental technology, and renewable energy reduce carbon emissions while negative shocks may result to elevated pollution levels in the long-run. However, adverse shocks in economic complexity and FDI cause increased pollution in the long run. Likewise, the short-run coefficient signs are also similar to the long-run coefficient signs but different in significance level and magnitude. This has paved the way for a well-designed policy for leading clean-energy investment countries should focus on structural change, FDI, technology and renewable energy consumption.
A sharp increase in economic and human development has multiplied the carbon intensity due to which there is a significant need of effective strategies in order to curb carbon emissions. Thus, the present study aims to examine the effective of green finance, eco-innovation, renewable energy output (REO), renewable energy consumption (REC), and carbon taxes on carbon dioxide (CO2) emissions in BRICS countries in the time of 2001–2020. Cross-sectional autoregressive distributed lag (CS ARDL) is used to test the connection among the variables. Empirical estimations of CS-ARDL approach validates the effectiveness of green finance, eco-innovation, REO, REC, carbon taxes, and industrialization as the relationship of these factors with carbon emissions is negative in nature in BRICS economies. Based on the evidences, the study recommends the formulation of environmentally friendly practices and advancement in green finances to mitigate carbon emissions.
After COP21, various economies start putting efforts to fulfill the pledge and achieve carbon neutrality. By doing so, scholars highlight several essential factors that can curb carbon emissions. In this lieu, the current study analyzes the role of technological innovation, carbon finance, environmental awareness, urbanization, and green energy like renewable energy consumption (REC) and renewable energy output (REO) on carbon neutrality in E7 countries covering the time span of 2006–2020. By employing CUP-FM and CUP-BC, it is revealed that technological innovation, carbon finance, environmental awareness, urbanization, REC, and REO have a positive connection with carbon neutrality in E7 countries. The study provides guidelines to the policymakers in developing policies regarding to obtain carbon neutrality using technological innovation, carbon finance, environmental awareness, and green energy.
Standards for Low-Carbon Energy Portfolios (LC-EPS) mandate that a certain percentage of a region's electricity generation originate from zero- or low-emissions sources. From 1995Q1 through 2020Q4, the study used the ARDL-Bounds testing technique to estimate coefficient parameters, Granger causality to draw causal inferences, and variance decomposition analysis to anticipate the factors that will have the greatest impact on carbon emissions in the US economy. Nuclear power significantly impacts carbon emissions, as seen by an inverted U-shaped environmental Kuznets curve, whereas long-term impact of innovation leads to lower emissions. On the other hand, exports of sophisticated technology reduce carbon emissions. Economic growth has a discernible effect on carbon emissions, nuclear power, innovation, and environmentally friendly financing. High-tech exports will likely impact carbon emissions most, followed by a demand for nuclear power, innovation, economic expansion, and sustainable finance for the next ten years. These results give policymakers helpful insight into how the US economy may reduce carbon emissions and fight climate change via renewable energy and green finance.
The objective of this study is to investigate the correlation between energy intensity and environment-related technology in industrialized countries. By utilizing panel data from 23 countries over a span of 32 years (1990–2021), this research aims to contribute to the comprehension of the role of green innovation in sustainable development. The study employs the Stochastic Impacts by Regression on Population, Affluence, and Technology model while controlling for variables including population growth, gross domestic product in purchasing power parity, Information and Communication Technology capital deepening, renewable energy consumption, and green innovation represented by research and development expenditure on environment-related technology. The results of the analysis, employing panel unit root tests, cross-sectional dependence tests, and a Method of Moments quantile regression, reveal that green innovation has a positive influence on diminishing energy intensity, with a more substantial impact at higher quantiles. Moreover, ICT capital deepening is determined to have a positive and noteworthy effect on reducing energy intensity. The findings of this study offer valuable insights for policymakers in their endeavours to accomplish sustainable development goals.
To ensure long-run sustainability, it is imperative to achieve the goal of zero-carbon emissions without compromising economic growth. Identifying whether BRICS economies, which are an attractive set of countries due to their rapid economic growth and high emissions, can shift towards sustainability with the support of policy measures, is a question which needs to be addressed. This article investigates the impact of emission trading schemes, energy innovation, technology transfer, population growth, and inflation on the economic performance of BRICS economies (2001–2020). The outcomes of the CS-ARDL and PMG estimators reveal that carbon taxes, carbon finance, energy innovation, technology transfer, population growth, and inflation have positive effects on economic performance. In light of the evidence, policy insights are recommended to achieve a win–win situation for economic and environmental performance.
Regional landslide susceptibility mapping (LSM) is essential for risk mitigation. While deep learning algorithms are increasingly used in LSM, their extensive parameters and scarce labels (limited landslide records) pose training challenges. In contrast, classical statistical algorithms, with typically fewer parameters, are less likely to overfit, easier to train, and offer greater interpretability. Additionally, integrating physics-based and data-driven approaches can potentially improve LSM. This paper makes several contributions to enhance the practicality, interpretability, and cross-regional generalization ability of regional LSM models: (1) Two new hybrid models, composed of data-driven and physics-based modules, are proposed and compared. Hybrid Model I combines the infinite slope stability analysis (ISSA) with logistic regression, a classical statistical algorithm. Hybrid Model II integrates ISSA with a convolutional neural network, a representative of deep learning techniques. The physics-based module constructs a new explanatory factor with higher nonlinearity and reduces prediction uncertainty caused by incomplete landslide inventory by pre-selecting non-landslide samples. The data-driven module captures the relation between explanatory factors and landslide inventory. (2) A step-wise deletion process is proposed to assess the importance of explanatory factors and identify the minimum necessary factors required to maintain satisfactory model performance. (3) Single-pixel and local-area samples are compared to understand the effect of pixel spatial neighborhood. (4) The impact of nonlinearity in data-driven algorithms on hybrid model performance is explored. Typical landslide-prone regions in the Three Gorges Reservoir, China, are used as the study area. The results show that, in the testing region, by using local-area samples to account for pixel spatial neighborhoods, Hybrid Model I achieves roughly a 4.2% increase in the AUC. Furthermore, models with 30 m resolution land-cover data surpass those using 1000 m resolution data, showing a 5.5% improvement in AUC. The optimal set of explanatory factors includes elevation, land-cover type, and safety factor. These findings reveal the key elements to enhance regional LSM, offering valuable insights for LSM practices.
Hydro-morphological processes (HMP, any natural phenomenon contained within the spectrum defined between debris flows and flash floods) are globally occurring natural hazards which pose great threats to our society, leading to fatalities and economical losses. For this reason, understanding the dynamics behind HMPs is needed to aid in hazard and risk assessment. In this work, we take advantage of an explainable deep learning model to extract global and local interpretations of the HMP occurrences across the whole Chinese territory. We use a deep neural network architecture and interpret the model results through the spatial pattern of SHAP values. In doing so, we can understand the model prediction on a hierarchical basis, looking at how the predictor set controls the overall susceptibility as well as doing the same at the level of the single mapping unit. Our model accurately predicts HMP occurrences with AUC values measured in a ten-fold cross-validation ranging between 0.83 and 0.86. This level of predictive performance attests for an excellent prediction skill. The main difference with respect to traditional statistical tools is that the latter usually lead to a clear interpretation at the expense of high performance, which is otherwise reached via machine/deep learning solutions, though at the expense of interpretation. The recent development of explainable AI is the key to combine both strengths. In this work, we explore this combination in the context of HMP susceptibility modeling. Specifically, we demonstrate the extent to which one can enter a new level of data-driven interpretation, supporting the decision-making process behind disaster risk mitigation and prevention actions.
Different driving forces govern the formation of distinct types of oil and gas accumulation and yield diverse oil and gas distributions. Complex oil and gas reservoirs in basins are commonly formed by the combination of multiple forces. It is very difficult but essential to identify driving forces and evaluate their contributions in predicting the type and distribution of oil and gas reservoirs. In this study, a novel method is proposed to identify driving forces and evaluate their contribution based on the critical conditions of porosity and permeability corresponding to buoyancy-driven hydrocarbon accumulation depth (BHAD). The application of this method to the Nanpu Sag of the Bohai Bay Basin shows that all oil and gas accumulations in the reservoirs are jointly formed by four driving forces: buoyance (I), non-buoyance (II), tectonic stress (III1) and geofluid activity (III2). Their contributions to all proven reserves are approximately 63.8%, 16.2%, 2.9%, and 17.0%, respectively. The contribution of the driving forces is related to the depth, distance to faults and unconformity surfaces. Buoyancy dominates the formation of conventional reservoirs above BHAD, non-buoyant dominate the formation of unconventional reservoirs below BHAD, tectonic stress dominates the formation of fractured reservoirs within 300 m of a fault, and geofluids activity dominates the formation of vuggy reservoirs within 100 m of an unconformity surface.
Different types of landslides exhibit distinct relationships with environmental conditioning factors. Therefore, in regions where multiple types of landslides coexist, it is required to separate landslide types for landslide susceptibility mapping (LSM). In this paper, a landslide-prone area located in Chongqing Province within the middle and upper reaches of the Three Gorges Reservoir area (TGRA), China, was selected as the study area. 733 landslides were classified into three types: reservoir-affected landslides, non-reservoir-affected landslides, and rockfalls. Four landslide inventory datasets and 15 landslide conditional factors were trained by three Machine Learning models (logistic regression, random forest, support vector machine), and a Deep Learning (DL) model. After comparing the models using receiver operating characteristics (ROC), the landslide susceptibility indexes of three types landslides were acquired by the best performing model. These indexes were then used as input to generate the final map based on the Stacking method. The results revealed that DL model showed the best performance in LSM without considering landslide types, achieving an area under the curve (AUC) of 0.854 for testing and 0.922 for training. Moreover, when we separated the landslide types for LSM, the AUC improved by 0.026 for testing and 0.044 for training. Thus, this paper demonstrates that considering different landslide types in LSM can significantly improve the quality of landslide susceptibility maps. These maps in turn, can be valuable tools for evaluating and mitigating landslide hazards.
Magmatism associated with oceanic subduction plays a dominant role in crustal growth during the Earth’s evolution. The Tannuola terrane, situated in the northern Central Asian Orogenic Belt (CAOB), is a key area to understanding oceanic subduction and initial collision processes in the northern CAOB. In order to investigate the switch from subduction to collision settings, detailed field mapping, zircon SHRIMP U-Pb geochronological and whole-rock geochemical studies of volcanogenic-sedimentary rocks from the Tannuola terrane were carried out. Zircon U-Pb ages indicate multi-stage volcanism lasted at least 30 Ma from ∼540 to ∼510 Ma, that can be divided into three events: the late Ediacaran (before ∼540 Ma), the early Cambrian (∼520 Ma) and the middle Cambrian (∼510 Ma). These ages are interpreted to the initial, main and final stages of oceanic subduction during the late Proterozoic – early Paleozoic. During the late Ediacaran, tholeiitic basalts with high εNd(t) values (from +7.4 to +8.5) were emplaced. Likely forming by the 10 %–30 % partial melting of spinel – garnet mantle source during slab subduction. During the early Cambrian, transitional from tholeiitic to calc-alkaline basaltic rocks with εNd(t) value (+5.6) and coeval intermediate–felsic volcanic rocks with similar εNd(t) values (+5.9 and +6.5) formed. The early Cambrian basaltic rocks are interpreted to be derived by 10 %–30 % partial melting of a depleted mantle source metasomatized by slab-derived fluids released from the subducting oceanic slab. The middle Cambrian calc-alkaline basaltic rocks with εNd(t) value of +6.2 might be emplaced as a result of low (5 %–10 %) degree partial melting of a metasomatized mantle followed by fractional crystallization of clinopyroxene and plagioclase. Associated intermediate-felsic volcanic rocks with εNd(t) values from +6.0 to +6.8 were formed through fractionation of the juvenile Neoproterozoic sources. The middle Cambrian volcanism is interpreted to be triggered by the slab break-off during the transition to a collisional setting.
The Coorg Block in southern Peninsular India is one of the oldest crustal blocks on Earth that preserves the evidence for continental crust formation during the Paleo-Mesoarchean through subduction-related arc magmatism, followed by granulite facies metamorphism in the Mesoarchean. In this study, we report for the first time, the ‘bar codes’ of a major Paleoproterozoic Large Igneous Province in the Coorg Block through the finding of mafic dyke swarms. The gabbroic dykes from the Coorg Block, dominantly composed of plagioclase-pyroxene assemblage, show a restricted range in SiO2 values of 50.04–51.27 wt.%, and exhibit a sub-alkaline tholeiitic nature. These rocks show relatively flat LREE and constant HREE patterns and lack obvious Eu anomalies. Trace element modeling suggests that the dyke swarm was fed from a melt that originated at a shallow mantle level in the spinel stability field. Zircon grains are rare in the gabbro samples and those separated from two samples yielded 207Pb/206Pb weighted mean dates of 2214 ± 12 Ma and 2221 ± 7 Ma. The grains show magmatic features with depleted LREE and enriched HREE and positive Ce and negative Eu anomalies. Baddeleyite grains were dated from five gabbro samples which yielded 207Pb/206Pb weighted mean ages ranging between 2217 ± 7 Ma and 2228 ± 10 Ma. The combined data show a clear age peak at ca. 2.2 Ga. The mafic dykes in the Coorg Block show geochemical similarities with ca. 2.2 Ga mafic dyke swarms in different regions of the Dharwar and other cratons in Peninsular India and elsewhere on the globe. The data also support the inference that the global mafic magmatism at ca. 2.2 Ga was linked with intracontinental rifting of the Archean cratons through mantle upwelling or plume activity. We correlate the mafic dyke swarms in the Coorg Block with attempted rifting of the Neoarchean supercontinent Kenorland.
The examination of fluctuations in the correlations between
An intramolecular isotopic study was conducted on natural gases collected from coal-derived gas reservoirs in sedimentary basins of China to determine their position-specific isotope distributions. The propane from the Turpan-Hami Basin exhibited negative ΔC-T (δ13Ccentral-δ13Cterminal) values ranging from −3.9‰ to −0.3‰, with an average of −2.1‰. Propane from the Ordos Basin, Sichuan Basin, and Tarim Basin showed positive ΔC-T values, with averages of 1.3‰, 5.4‰ and 7.6‰, respectively. Position-specific carbon isotope compositions reveal the precursors and the propane generation pathways in the petroliferous basins. Propane formed from the thermal cracking of Type III kerogen has larger δ13Ccentral and δ13Cterminal values than propane from Type I/II kerogen. The precursor for natural gases collected in this study is identified to be Type III kerogen. Comparing our data to calculated results for thermal cracking of Type III kerogen, we found that propane from the low-maturity gas reservoir in the Turpan Basin was generated via the i-propyl radical pathway, whereas propane from the Sulige tight gas reservoir in the Ordos Basin was formed via the n-propyl radical pathway. δ13Cterminal values covered a narrow range across basins, in contrast to δ13Ccentral. The terminal carbon position in propane is less impacted by microbial oxidation and more relevant to maturity levels and precursors. Thus, δ13Cterminal has a good potential to infer the origin and maturity level of natural gas. In examining post-generation processes, we proposed an improved identification strategy for microbial oxidation of natural gases, based on the position-specific carbon isotope distributions of propane. Samples from the Liaohe Depression of the Bohai Bay Basin and the Sichuan Basin were detected of post-generation microbial oxidation. Overall, position-specific carbon isotope composition of propane provides new insights into the generation mechanism and post-generation processes of natural gas in the geological period at the atomic level.
Densely populated region of Ganga Plain is facing aquifer vulnerability through waterborne pollutants and groundwater stress due to indiscriminate abstraction, causing environmental and socio-economic instabilities. To address long-term groundwater resilience, it is crucial to understand aquifer heterogeneity and connectivity, groundwater recharge sources, effects of groundwater abstraction etc. In this context, present study aims to understand factors responsible for vertical and spatial variability of groundwater chemistry and to identify groundwater recharge sources in an intensively exploited agrarian region of the Ganga Plain.
Inter-provincial carbon compensation is an important means for a country to realize regional environmental protection and achieve coordinated regional development and realize the carbon neutral goal. It is easier to realize inter-provincial carbon compensation compared with the national level. Based on the multi-regional input-output model and the input–output data of 30 provinces in China, this study measured the carbon transfer in, carbon transfer out and net carbon transfer of each province, and based on the undesirable slacks-based measurement model under the common frontier, the provinces were given the shadow price of carbon emission in line with the situation of the local economic development, resource endowment, and industrial structure, and based on which, the amount of carbon compensation of each province was measured. The results show that: China's provinces and regions have a larger share of trade-implied carbon emissions; the net carbon transfer in areas mainly concentrated in the traditional energy provinces, which provide industrial products for other regions and undertake the transfer of carbon emissions, and become the main carbon compensation recipient areas; the net carbon transfer out is mainly concentrated in the economically developed and densely populated areas such as Beijing–Tianjin region and the eastern and southern coasts, which satisfy the end-consumption by purchasing a large number of industrial products and generate a large amount of carbon emissions. Transfer out; becoming the main carbon offset payment area. Based on the results of this study, it is proposed to improve the national provincial carbon offset mechanism and implement a differentiated and synergistic carbon emission reduction cooperation approach. The research program of this study can provide a reference for the development of inter-regional carbon offset programs.
The Ediacaran–Cambrian transition witnessed some of the most important biological, tectonic, climatic and geochemical changes in Earth’s history. Of utmost importance for early animal evolution is the likely shift in redox conditions of bottom waters, which might have taken place in distinct pulses during the late Ediacaran and early Paleozoic. To track redox changes during this transition, we present new trace element, total organic carbon and both inorganic and organic carbon isotopes, and the first iron speciation data on the Tamengo and Guaicurus formations of the Corumbá Group in western Brazil, which record important paleobiological changes between 555 Ma to < 541 Ma. The stratigraphically older Tamengo Formation is composed mainly of limestone with interbedded marls and mudrocks, and bears fragments of upper Ediacaran biomineralized fossils such as Cloudina lucianoi and Corumbella werneri. The younger Guaicurus Formation represents a regional transgression of the shallow carbonate platform and is composed of a homogeneous fine-grained siliciclastic succession, bearing meiofaunal bilateral burrows. The new iron speciation data reveal predominantly anoxic and ferruginous (non-sulfidic) bottom water conditions during deposition of the Tamengo Formation, with FeHR/FeT around 0.8 and FePy/FeHR below 0.7. The transition from the Tamengo to the Guaicurus Formation is marked by a stratigraphically rapid drop in FeHR/FeT to below 0.2, recording a shift to likely oxic bottom waters, which persist upsection. Redox-sensitive element (RSE) concentrations are muted in both formations, but consistent with non-sulfidic bottom water conditions throughout. We interpret the collected data to reflect a transition between two distinct paleoenvironmental settings. The Tamengo Formation represents an environment with anoxic bottom waters, with fragments of biomineralized organisms that lived on shallower, probably mildly oxygenated surficial waters, and that were then transported down-slope. Similar to coeval successions (e.g., the Nama Group in Namibia), our data support the hypothesis that late Ediacaran biomineralized organisms lived in a thin oxygenated surface layer above a relatively shallow chemocline. The Guaicurus Formation, on the other hand, records the expansion of oxic conditions to deeper waters during a sea level rise. Although the relationship between global biogeochemical changes and the activities of early bioturbators remains complex, these results demonstrate an unequivocal synchronous relationship between oxygenation of the Corumbá basin and the local appearance of meiofaunal bioturbators.
Detrital geochronology fundamentally involves the quantification of major age ranges and their weights winthin an age distribution. This study presents a streamlined approach, modeling the age distribution of detrital zircons using a normal mixture model, and employs the Expectation-Maximization (EM) algorithm for precise estimations. A method is introduced to automatically select appropriate initial mean values for EM algorithm, enhancing its efficacy in detrital geochronology. This process entails multiple trials with varying numbers of age components leading to diverse k-component models. The model with the lowest Bayesian Information Criterion (BIC) is identified as the most suitable. For accurate component number and weight determination, a substantial sample size (n > 200) is advisable.
The Haoyaoerhudong gold deposit, located in the northwestern part of the North China craton (NCC), has produced over 120 metric tonnes (t) of gold since 2007. It has a total reserve of > 240 t at average gold grade of 0.62 g/t, making it one of the largest open pit gold mines in China. The steeply dipping, large-tonnage, low-grade, vein- or veinlet-type gold orebodies are hosted in strongly-deformed Mesoproterozoic carbonaceous schist of the Bayan Obo Group. The laminated/boudinaged veins/veinlets in the sinistral ductile–brittle shear zones are dominated by quartz, biotite, gold-bearing löllingite, pyrrhotite, (arseno)pyrite, with minor native gold, titanite and xenotime. In this paper, we present new in situ U–Pb geochronological data on magmatic zircon from the pre-ore dikes, on metamorphic and hydrothermal xenotime, and on hydrothermal titanite from the hydrothermally altered carbonaceous schist and auriferous quartz–sulfides veins/veinlets, as well as He-Ar isotopic analysis on gold-bearing (arseno)pyrite in the syn-ore stage. The metamorphic xenotime U–Pb age of 426 ± 6.0 Ma (2σ) records a regional metamorphic event, possibly related to the accretion of the Bainaimiao arc onto the NCC. Two pre-ore andesitic dikes yielded similar emplacement ages at ∼ 278 Ma constrained by laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) U–Pb zircon data. Hydrothermal xenotime grains from the altered carbonaceous schist and auriferous quartz–sulfides veins yielded U–Pb ages of 256.0 ± 4.1 Ma (2σ) and 254.4 ± 2.1 Ma (2σ), respectively, overlapping with that of the hydrothermal titanite at 255.4 ± 0.8 Ma (2σ) from the laminated quartz–sulfides veinlets. This indicates that the gold mineralization occurred at ca. 255 Ma. The ∼ 255 Ma gold mineralization age is much younger than the previously reported Early–Middle Permian regional magmatic activity (ca. 291 Ma to 268 Ma), and may be associated with the regional sinistral strike-slip event in the late orogenic cycle related to the collision between the Siberian craton and the NCC. The 3He/4He (R/Ra) and 40Ar/36Ar values of the gold-bearing (arseno)pyrite are 0.04 to 0.09 (average = 0.07) and 375.8 to 2023 (average = 1045), which reveal the ore-forming fluids dominantly originated from the crustal rocks, with limited involvement from the mantle. Collectively, our new geochronological data, noble gas isotopic analyses, and geological evidence support a typical orogenic gold deposit model at Haoyaoerhudong.
The Itmurundy Zone of Central Kazakhstan is a key structure in the core of the Kazakh Orocline representing a typical Pacific-type orogenic belt hosting accretionary complex, ophiolite massifs and serpentinite mélange. The main controversies in the existing tectonic models of the Itmurundy Zone are about the timing of subduction and accretion, the direction and kinematics of subduction and the number of oceanic plates. A new model for the early Paleozoic tectonic story of the Itmurundy Zone is postulated in this paper, based on new detailed geological and U–Pb detrital zircon age data, combined with previously documented geological, U–Pb age, microfossil, geochemical and isotope data from igneous rocks, deep-sea sediments and greywacke sandstones. The present study employs the Ocean Plate Stratigraphy (OPS) model to explain the tectonic processes involved in the evolution of the Itmurundy Zone and to present a holistic story of Ordovician oceanic plate(s), which accretion formed an accretionary complex. The detailed mapping allows distinguishing three types of OPS assemblages: (1) Chert-dominated, (2) OIB-hosting, and (3) MORB-hosting. The U–Pb ages of detrital zircons from sandstones of OIB and Chert types show unimodal distributions with similar main peaks of magmatism at 460–455 Ma in the provenance, and their maximum depositional ages (MDA) span 455–433 Ma. Two samples from OPS Type 3 show the peaks of magmatism both at ca. 460 Ma and the MDA of 452 Ma and 459 Ma, respectively. The MDA of sandstones and microfossils data from chert show the younging of strata to the south and SE in Types 1 and 2 and to NEE for Type 3 (in present coordinates) suggesting double-sided subduction to the NNW and SEE and, accordingly, the co-existence of pieces of two oceanic plates in Ordovician time. The U–Pb zircon data from both igneous and clastic rocks indicate a period of subduction erosion in early Ordovician time. As a whole, the accreted OPS units of the Itmurundy Zone record the timing of subduction and accretion from the early Ordovician to the early Silurian, i.e., 60 Ma at shortest.
The application of Artificial Intelligence in various fields has witnessed tremendous progress in the recent years. The field of geosciences and natural hazard modelling has also benefitted immensely from the introduction of novel algorithms, the availability of large quantities of data, and the increase in computational capacity. The enhancement in algorithms can be largely attributed to the elevated complexity of the network architecture and the heightened level of abstraction found in the network's later layers. As a result, AI models lack transparency and accountability, often being dubbed as “black box” models. Explainable AI (XAI) is emerging as a solution to make AI models more transparent, especially in domains where transparency is essential. Much discussion surrounds the use of XAI for diverse purposes, as researchers explore its applications across various domains. With the growing body of research papers on XAI case studies, it has become increasingly important to address existing gaps in the literature. The current literature lacks a comprehensive understanding of the capabilities, limitations, and practical implications of XAI. This study provides a comprehensive overview of what constitutes XAI, how it is being used and potential applications in hydrometeorological natural hazards. It aims to serve as a useful reference for researchers, practitioners, and stakeholders who are currently using or intending to adopt XAI, thereby contributing to the advancements for wider acceptance of XAI in the future.
The Tengchong volcano field (TVF), situated at the southeastern margin of the Tibetan Plateau, holds crucial information regarding Cenozoic volcanic activities and geotectonic evolution of the SE Tibet. To provide new constraints on petrogenesis and evolution of the Tengchong volcanism, here we conducted copper (Cu) elemental and isotopic analyses on a suite of samples that document the evolution from basalts to andesites in the TVF. The basalts are Cu-depleted (29.7–36.9 ppm) and have higher δ65Cu values (0.19‰–0.40‰, mean = 0.31‰ ± 0.05‰; n = 11) than those of mid-ocean ridge basalts (MORBs, ∼0.09‰) and the mantle (∼0.06‰) as well as the majority of island arc lavas. Along with the low Cu/Zr ratios, these characteristics are interpreted to reflect the fractionation of isotopically light sulfides in the S-saturated systems during magma ascent, rather than source heterogeneity induced by recycled materials and redox reactions. Compared with the basalts, the andesites have slightly lower Cu contents (14.4–29.4 ppm) and lighter Cu isotopic compositions (mean = –0.14‰ ± 0.06‰; n = 13). These differences cannot be attributed to progressive sulfide fractionation of basaltic magmas but require the assimilation of lower crustal materials with low δ65Cu values during evolution of the andesitic magmas. Our results collectively suggest that Cu isotopes can provide valuable insights into magma origin and evolution.
Numerous scientific fields are facing a replication crisis, where the results of a study often cannot be replicated when a new study uses independent data. This issue has been particularly emphasized in psychology, health, and medicine, as incorrect results in these fields could have serious consequences, where lives might be at stake. While other fields have also highlighted significant replication problems, the Earth Sciences seem to be an exception. The paucity of Earth Science research aimed at understanding the replication crisis prompted this study. Specifically, this work aims to fill that gap by seeking to replicate geological results involving various types of time-series. We identify and discuss 11 key variables for replicating U-Pb age distributions: independent data, global sampling, proxy data, data quality, disproportionate non-random sampling, stratigraphic bias, potential filtering bias, accuracy and precision, correlating time-series segments, testing assumptions and divergent analytical methods, and analytical transparency. Even while this work primarily focuses on U-Pb age distributions, most of these factors (or variations of them) also apply to other geoscience disciplines. Thus, some of the discussions involve time-series consisting of εHf, δ18O-zircon, 14C, 10Be, marine δ13C, and marine δ18O. We then provide specific recommendations for minimizing adverse effects related to these factors, and in the process enhancing prospects for replicating geological results.
