Natural resources represent the base of our living and the entire economic activity. Their depletion is a major challenge for the economic development of both developed and developing economies. Their efficient use is an indispensable requirement and must be the aim of the public policies designed by the authorities worldwide. In this research, we have investigated the impact of the natural resources rent on the economic growth in some major wealthy economies of the world (P5 + 1 countries namely: US, UK, France, China, Russia, and Germany). We have applied a quantile-on-quantile regression to analyse this impact on different quantiles and a cross-sectional autoregressive distributed lag (CS-ARDL) approach for the panel of these six countries. The Dumitrescu-Hurlin panel causality test was also used to check the causality between natural resource rents and economic growth in these countries. Results show a negative relationship between natural resources rent and economic growth for the panel but a different impact on quantiles in each country. Only for China and the US, a positive effect can be noticed for both lower and higher quantiles of natural resources and economic growth. The Dumitrescu-Hurlin causality test shows that natural resources can predict economic growth only in China, the U.S., and the panel. In contrast, no causality was found for the other four countries included in the panel. We suggest that nations invest in wind and solar projects, use biofuels and nuclear energy, introduce a temporary profit tax to protect consumers from escalating energy prices, and increase energy efficiency in buildings and industry. Businesses would benefit from a regulatory framework that is uniform and exhaustive, as well as easier to traverse and more receptive to innovation and creativity. Public-private partnership investments in innovation, innovation incentives, and environmental sector opportunities may foster long-term economic growth.
New energy development is essential to achieving carbon peaks and neutrality and promoting green technological innovation. Identifying the causal relationship between new energy demonstration city construction and green technological innovation is crucial for the expansion and promotion of new energy demonstration cities. In this study, we take the construction of new energy demonstration cities as a quasi-natural experiment, study their impact on green technological innovation using difference-in-difference (DID), and conduct a robustness test using DID after propensity score matching (PSM-DID). The research results indicate the following: First, energy structure optimization can significantly improve the level of urban green technological innovation (this result was shown to be valid using PSM–DID and other tests involving the effects of placebo and instrumental variables). Second, new energy demonstration city construction mainly improves the level of urban green technological innovation through technology research and development, the improvement of the industrial innovation environment, and the promotion of environmental performance. Third, the impact of energy structure optimization on green technological innovation has regional, financial, and economic development heterogeneity. Finally, new energy demonstration city policy affects the flow of capital, labor, technology, and other production factors to pilot areas according to new energy demonstration city policy, forming a “siphon effect”. The carbon reduction effect of new energy demonstration city construction is greater than its pollution reduction effect. Given the results of the study, policy recommendations to promote the expansion of new energy demonstration cities are proposed.
Considering that previous literature has mainly focused on the impact of the digital economy (DE) on environmental degradation, ignoring the role of natural resources, this study uses two key factors (natural resource rent and anticorruption regulation) as threshold variables to reveal the effect of natural resources on the association between DE and carbon dioxide (CO2) emissions. In doing so, the study covers 97 countries, uses annual data between 2003 and 2019, and applies a panel threshold model. The outcomes present that the influence of the DE on CO2 emissions has a single-threshold effect (i.e., there is an inverted U-shaped link between the DE and CO2 emissions) when natural resource rent is the threshold variable. Specifically, the DE significantly increases CO2 emissions when the natural resource rent is at a low-to-medium level, but the DE suppresses CO2 emissions growth when natural resource rent exceeds the threshold. Moreover, the DE drives overall CO2 emissions growth when anticorruption regulation is the threshold variable and there are double thresholds for its impact on CO2 emissions. Specifically, a rise in anticorruption regulation initially exacerbates the contribution of DE impact on CO2 emissions and then weakens it over time. Based on the results, the study proposes various implications, such as formulating a DE development strategy, considering natural resources in the development of the DE, and strengthening anti-corruption efforts in the field of environmental protection.
The current study examines the significant determinants of electricity consumption and identifies an appropriate model to forecast the electricity price accurately. The main contribution is focused on eastern region of Saudi Arabia, a relatively hottest geographical area full of energy resources but with different electricity consumption patterns. The relative irrelevance of temperature as predicting factor of electricity consumption is quite surprising and contradicts the previous studies. In the eastern region, electricity price has negative association with electricity consumption. While comparing traditional and machine learning, it is found that machine learning techniques offer better predictability. Amongst the machine learning techniques, the support vector machine has the lowest errors in forecasting the electricity price. Additionally, the support vector machine approach is used to forecast the trend of carbon emissions caused by electricity consumption. The findings have policy implications and offer valuable suggestions to policymakers while addressing the determinants of electricity consumption and forecasting electricity prices.
As a global strategic reserve resource, rare earth has been widely used in important industries, such as military equipment and biomedicine. However, existing analyses based solely on the total volume of rare earth trade fail to uncover the underlying competition and dependency dynamics. To address this gap, this paper employs the principles of trade preference and import similarity to construct dependency and competition networks. Complex network analysis is then employed to study the evolution of the global rare earth trade network from 2002 to 2018. The main conclusions are as follows. The global rare earth trade follows the Pareto principle, and the trade network shows a scale-free distribution. China has emerged as the world’s largest importer and exporter of rare earth since 2017. In the dependency network, China has become the most dependent country since 2006. The result of community division shows that China has separated from the American community and formed new communities with the Association of Southeast Asian Nations (ASEAN) countries. The United States of America has formed a super-strong community with European and Asian countries. In the competition network, the distribution of competition intensity follows a scale-free distribution. Most countries face low-intensity competition, but there are numerous competing countries. The competition related to China has increased significantly. Lastly, the competition source for the United States of America has shifted from Mexico to China, resulting in China, the USA, and Japan becoming the core participants in the competition network.
In this study, the relationships between five renewable energy sub-sectors markets and the geopolitical risk (GPR) and economic uncertainty indices (EUI) were examined using daily data from March 30, 2012, to April 1, 2022. Convergent cross mapping results show that the renewable energy indices have definite relationships with the GPR and EUI. The renewable energy indices show differences in response directions, speed and trends for a standard information difference impulse from the GPR and the EUI. A positive dynamic conditional correlation between renewable energies and EUI was observed in the first and second waves of the COVID-19 outbreak. In contrast, there was a relatively decreased effect for two risk indices during the Russia–Ukraine conflict of February–March 2022. Our results show that renewable energy may act as a time-varying hedge against economic uncertainty and GPR owing to its safe-haven properties at various scales. Moreover, building more secure and reliable renewable energy systems can help countries to increase their energy independence, which protects them against the risks of political and economic uncertainty.
Inorganic resources can be detrimental to the environment when exploited. In comparison, organic resources help balance the soil's carbon and nitrogen (C/N) ratio, enhance soil fertility and benefit ecological protection. Current climate crises, rapid urbanization, and fast population growth are causing many natural forests to be converted to agricultural and industrial lands to fulfill ever-increasing food and developmental requirements. Application of different bio-resources becomes necessary for sustainable productivity of available lands. This study explores the effects of various organic amendments on the growth, morpho-physiological and biochemical attributes of three leguminous tree species: Dalbergia sissoo, Vachellia nilotica, and Acacia ampliceps, concerning sustainable productivity. One-year-old healthy, disease-free, and homogenous seedlings were used as study material in a greenhouse pot experiment. Four organic amendments, i.e., compost (CMP), cow dung (CD), poultry manure (PM), and biochar (BC), along with a control (CK) treatment, were applied. Results showed that all the organic amendments performed significantly better (P < 0.05) than CK. CD produced the most significant results, followed by BC application, while PM influence was the least. Among all treatments and species, the maximum values of root length, root biomass, chlorophyll content, carotenoids, catalase, and total phenolic content were recorded under the CD treatment. Whereas for plant height and collar diameter, no big differences were observed between CD and BC (P = 0.054). While comparing species, V. nilotica growth was significantly enhanced under organic amendments, followed by A. ampliceps. Combined and comparative results of studied parameters conclude that CD and BC were the most effective organic amendments, which greatly improved the growth of experimental leguminous tree species; this makes these two biofertilizers a powerful tool for sustainable agricultural productivity. Our study contributes toward an enhanced understanding of plant's morpho-physiological responses, biochemical growth patterns, controls, and activities under different bio-fertilizers.
Mangrove ecosystems have important ecological and economic values, especially their ability to store carbon. However, in recent years, human disturbance has accelerated mangrove degradation. Among them, the emission of pollutants cannot be ignored. It is of great significance for carbon emission reduction and ecological protection to study the impacts of different pollutants on mangroves and their carbon stocks. Based on the remote sensing data of coastal areas south of the Yangtze River in mainland China, this paper builds the ensemble learning model Random Forest (RF) and Gradient Boosting Regression (GBR) to empirically analyse the relationship between industrial wastewater, industrial sulfur dioxide (SO2), PM2.5 and mangrove forests. The results show that the pollutant concentration of meteorological normalisation is more stable. The importance of pollutants presents regional heterogeneity. The area of mangroves in different cities and the corresponding total carbon stocks show different trends with the increase or decrease of pollutants, and there is a dynamic balance between urban pollutant discharge and mangrove growth in some cities. The research in this paper provides an analysis and explanation from the perspective of machine learning to explore the relationship between mangroves and pollutants and at the same time, provides scientific suggestions for the formulation of future pollutant emission policies in different cities.
This study aims to demystify the role of green energy and green technology in establishing the nexus between behavioural intentions of tourists, technologies, and digital payments by using Perceived value (PV), Compatibility (CO), Perceived Enjoyment (PE), and Social Influence (SI) as a predictor variables, Trust (TR) and Satisfaction (SA) as a mediating variables and Behavioural Intentions (BI) as an outcome Variable. For the empirical estimation, we employ smart PLS-SEM, TAM (Technology Acceptance Model) and SPSS and Tested the LCC hypothesis. Key findings suggest that green energy and perceived value have the highest positive impact on tourists' trust towards digital payments followed by compatibility, social influence and perceived enjoyment. Similarly, tourists’ satisfaction and green technology is one of the important determinants of choosing any digital mode of payment, is mostly influence by perceived value, perceived enjoyment, compatibility and social influence. Moreover, if we choose between trust and satisfaction, trust plays a significant role in exploring the behavioural intentions of tourists about green energy and green technology followed by tourists’ satisfaction. In addition, Tourists’ trust and satisfaction are highly correlated and influence each other. The study offers novel policy implications in terms of use of green technology and green energy in enhancing trust and satisfaction of tourists in order to deeper understanding of different dimensions of digital payments and M−wallets, and allowing them to explore the long-term value inherent of digital payments and M−wallets.
The prediction performance of traditional forecasting methods is low due to the high level of complexity in a series of energy prices. The present study attempts to compare the traditional regression, machine learning tools and hybrid models to conclude the outperforming model. The first step is to propose the effective denoising technique for Tadawul energy index, which has confirmed the superiority of CSD based denoising. However, we use the CSD-ARIMA, CSD-ANN, and CSD-RNN as hybrid models. As a result, CSD-RNN outperforms both other models in terms of MSE, MAPE, RMSE and Dstat. The findings are useful for policy makers, investors and portfolio managers to forecast the energy trends, and hedge the portfolio risk accordingly.
The green transformation of energy consumption is beneficial for promoting green development in China. This study constructed a green energy consumption evaluation index system and measured the green energy consumption levels in 30 provinces of China from 2000 to 2019 using the fuzzy comprehensive evaluation method. This study further employed the spatial Durbin model to examine influencing factors and spillover effects of green energy consumption. The results showed that, temporally, China’s green energy consumption levels had a fluctuating upward trend. While, spatially, the overall levels of green energy consumption in China showed apparent characteristics of “high in the west and low in the east”. In terms of influencing factors, environmental regulations played an important role in promoting green energy consumption in the region, while economic development, opening up, and industrial structure had considerably inhibiting effects. Additionally, economic development, opening up, and industrial structure of neighboring regions showed marked positive spillover effects, while urbanization level and technological innovation showed substantial negative spillover effects. The regional heterogeneity test results showed that environmental regulation and industrial structure rationalization were the important factors for promoting green energy consumption in the eastern region, environmental regulation played an important driving role in the central region, and opening to the outside world and technological innovation helped improve the level of green energy consumption in the western region.
Low carbon productivity has been identified as a key direction for China’s future development. As an important driving force for economic growth, the question of whether digital finance that is reliant on digital technology can support the development of a low-carbon urban economy remains unresolved. Based on the carbon productivity measured by panel data from 201 cities for the period 2011–2020, this study applies the spatial Dubin model and threshold regression model to explore the impact of digital finance on carbon productivity, yielding the following key conclusions. First, the spatial distribution heterogeneity of carbon productivity in China’s eastern region is higher than that in the western region, and both productivity and digital finance are characterized by high (low)–high (low) dotted spatial agglomeration. Second, digital finance can significantly improve carbon productivity via two transmission channels: the human capital and marketization effects. At the same time, digital finance exerts a spatial spillover effect on carbon productivity, and rising local digital finance levels will increase carbon productivity in neighboring areas. Heterogeneity analysis indicates that the spillover effect of digital finance in urban agglomerations and eastern regions is more significant. Third, fixed-asset investment has a positive nonlinear moderating effect on digital finance, thus improving carbon productivity. When the per capita investment in fixed assets does not exceed 682.73 yuan, digital finance exerts only a limit pulling effect on carbon productivity; when it is higher than this value, the pulling effect is intensified.
This paper investigates the effect of the circular economy on CO2 emissions growth by considering the role of energy transition, climate policy stringency, industrialization, and supply chain pressure from 1997 to 2020 using panel quantile Autoregressive Distributed Lags (QARDL) and the panel PMG. We employ cointegration association in the long run among the variables, and the results of the two models confirm this. Findings reveal that circular economy and climate policy stringency significantly negatively impact carbon emissions. On the other hand, the energy transition, industrialization, and supply chain pressures are crucial to determining CO2 emissions in the short and long run. The finding further explores that municipal waste generation recycling is considerable at the mean and upper 90th quantiles than the lower quantile. Therefore, the empirical results of the current study provide acumens for policymakers of advanced economies and emerging markets to maintain the balance among circular economy, energy transition, environmental policy stringency, and supply chain pressure for reducing CO2 emissions without halting economic growth and sustainable development. Furthermore, practical implications are reported through the lens of carbon neutrality and structural changes.
Natural resources, climate change, and sustainable development are critical and simultaneously interrelated issues. This study investigates the interdependence between raw minerals material and sea level rise, considering the role of economic performance and material footprint employing wavelet locale multiple correlations from 1970 to 2019. The results provide strong evidence for cross-correlation of climate change with mineral resources, economic output, and domestic material consumption (biomass-fossil, metal, and non-metal) localized at the high frequency-time domain. However, the outcomes provide weak evidence for the association between bivariate time series at low frequency, which is a limitation in the short term. Based on the results, policymakers should implement effective environmental taxes and invest in cutting-edge technologies to optimize clean energy and mineral resources in a sustainable manner.
Sustainable development goals (SDGs) and fossil energy are the core elements of almost all major challenges and opportunities for achieving social development. Particularly, energy sustainability has become one of the pivotal drivers of China's economy. This study constructed a comprehensive evaluation index system for the provincial-level sustainable development of fossil energy in China covering three major dimensions (socio-economic, resource, and environmental). Moreover, a set of criteria for measuring the SDGs of fossil energy at the national level in China was developed. Based on the provincial panel data collected from 30 provinces from 2010 through 2019, a spatial econometric model was applied to empirically evaluate the effects of SDGs on fossil energy consumption. The results showed that the SDGs not only promote the reduction of fossil energy consumption with substantial negative spatial spillover effects, but also revealed differences between northern and southern China. To promote the early achievement of sustainable fossil energy development in China, the transformation and upgradation of fossil energy systems should be conducted early and inter-regional cooperation should be strengthened according to local conditions to jointly achieve the SDGs.
Copper is one of the most important minerals that has extensive use in environment-friendly technologies and renewable energy generation. The global urgency for environmental and ecological conservation through renewable energy transition has considerably enhanced the importance of copper and articles thereof. Chile is a major producer of copper. It contributes more than one-third to global supply. Therefore, this study explores the export flow of Chilean copper in response to increasing demand side conditions in major 24 trading partners from 2002 to 2020. This objective is realized by constructing an augmented model for import demand that incorporates bilateral real exchange rate along with real GDP, environmental innovation, and renewable energy transition in major import markets. The estimated results of panel quantiles via moments techniques reveal a significant positive impact with increasing coefficients at higher quantiles, while environmental innovation and renewable energy transition in trading partners show significant positive impact with decreasing values of coefficients at higher quantiles. The findings urge Chile to enhance production capacity of copper and other critical mineral and improve participation in global value chain to meet sharply increasing copper demand from environmental innovation and renewable energy transition.
As the extraction and usage of natural resources continue to be a double-edged sword – supporting economic growth but deteriorating the environment- we study the impact of natural resource mining on sustainable economic development in the largest (PPP) economy – China. We use province-level data from 2001 to 2020 and employ econometric panel techniques, such as fixed effects, two-stage least squares, and a battery of robustness tests. We further explore the potential effects of education and green innovation in mitigating/exacerbating the role of natural resources in the Chinese provincial economy. The results show that: (1) Natural resource mining hurts sustainable development, verifying the “resource curse” effect. (2) Green innovation and education restrain the negative impact of resource mining on sustainable development, turning the curse into a blessing. (3) A regional heterogeneity is observed in the impact of resource mining on sustainable development, showing more significant effects in the Western and low-urbanized regions. (4) Green innovation and education can assuage the curse effect of natural resources into gospel effect. Policy implications and recommendations are proposed in light of the findings to promote sustainable economic development in China.
This study explores the connections between renewable energy consumption (REC), non-renewable energy consumption (NREC), gross fixed capital formation (GFCF), the labor force (LF), and economic growth (GDP) in Renewable Energy Country Attractiveness Index (RECAI) countries for 1991–2016. We quantify the nexus between REC, NREC, and GDP while utilizing a production model framework and including the measures of labor and capital, for suggesting a phase-wise strategy to attain the sustainable development goals. We use robust methodologies including Lagrange Multiplier (LM) panel unit root tests with trend shifts, Westerlund cointegration test, LM bootstrap technique for cointegration with breaks, continuously updated fully modified (CUP-FM) and continuously updated bias-corrected (CUP-BC) estimators, Augmented Mean Group (AMG) approach, fully modified ordinary least squares, dynamic ordinary least squares, Canonical Cointegrating Regression (CCR), and panel causality test proposed by Canning & Pedroni. We compute non-parametric time-varying coefficients with fixed effects for seeing the impact of GFCF, LF, REC, and NREC on GDP. Our results press upon policymakers to shift toward clean energy and REC for attaining the environmental goals (SDGs 6, 7, 13, and 15) and the economic goals (SDGs 1, 2, 8, and 10). While this shift would help developed economies, which have already attained the economic goals, to progress on the front of environmental goals, it would enable developing countries to progress on both fronts in a balanced manner.
With the increasing global water scarcity, wastewater irrigation has become widespread, but it can have detrimental ecological consequences. Although wastewater contains valuable nutrients for plants, improper treatment or the use of untreated wastewater in irrigation can negatively impact soil fertility and plant growth. This study is divided into two parts: firstly, a phytosociological survey was conducted to identify plant species with the highest importance value index (IVI) in the vicinity of wastewater-irrigated areas. Secondly, a comparative morpho-anatomical analysis was carried out to investigate the morpho-anatomical adaptations of the species with the highest IVI under wastewater irrigation compared to normal water irrigation. The results of the phytosociological survey revealed the presence of 51 plant species in the vicinity of the wastewater-irrigated areas, with varying relative densities and coverage. Datura inoxia exhibited the highest IVI (28.79), followed by Xanthium strumarium (24.34), while Lippia nodiflora showed the lowest IVI (1.86). The morphological growth of D. inoxia was superior under normal water irrigation, but the average root length was greater under wastewater irrigation. Regarding cell anatomy, cell thickness and cell area characteristics in the dermal, ground, and vascular tissues of the stem and root tended to be greater under wastewater treatment. However, the opposite trend was observed in leaf anatomical analysis, possibly due to the combined effect of wastewater and climatic conditions. Stem and root xylem thickness were greater under wastewater irrigation, whereas phloem thickness was higher under normal water irrigation. The number of vascular bundles in the stem, root, and leaf was higher under wastewater treatment compared to normal water irrigation, but their arrangement was circular in the latter and scattered under wastewater irrigation. Datura inoxia demonstrated strong adaptive potential under wastewater irrigation, as indicated by its highest relative density, coverage, and IVI, suggesting its suitability for phytoremediation. However, due to the low relative density, coverage, and IVI of many other species, the use of untreated wastewater for irrigation cannot be appreciated.
The worldwide tremor of environmental degradation commonly represents the escalation of emissions levels and ecological footprints that harm the planet's biocapacity. This is because of using gigantic non-renewable energy resources, urbanization stream and massive economic activities in the major industrialized nations. Amid this situation, we investigate the influence of disaggregated energy measures, e.g., renewable, and nuclear energy, income growth and urbanization on the load capacity factor (biocapacity divided by the ecological footprint) of major nuclear power countries, such as France, the USA, Canada, China, and Russia during 1990–2021. To this end, we utilize the CS-ARDL procedure because of the endogeneity, common correlation, non-stationarity in data and heterogeneity in panel units. We contribute to considering the supply side dynamic of environmental degradation parameter, the load capacity, from the perspective of the top nuclear power nations that deviates our analysis from the prevailing scholarly works. However, our findings confirm a significantly positive impact of renewable and nuclear energy on the load capacity factor in improving environmental safety. Besides, economic growth and urbanization negatively affect the load capacity dynamics in spurring environmental degradation. Our findings are robust across an alternative estimation technique, namely the Dumitrescu and Hurlin (DH) causation analysis. Therefore, we recommend formulating pragmatic policies to deter the detrimental effects of income and urbanization by properly utilizing sustainable energy resources to conserve the natural environment.
The current study attempts to compare the hybrid artificial intelligence models to forecast the environmental externalities in Saudi Arabia. We have used the denoising based artificial intelligence models to construct hybrid models. While comparing the denoising techniques, the CSD-based denoising has outperformed. However, we have used the CSD-based hybrid models. CSD-ANN and CSD-RNN are used for denoising-based artificial intelligence models, whereas CSD-ARIMA is used for denoising-based traditional models. All these models are used to check and compare their performance in terms of level and direction of prediction for PM10. The results show that the CSD-based ANN model has a higher predictability for PM10 levels in Saudi Arabia due to low error values and higher Dstat values. In comparing original and forecasted data, the superiority of CSD-ANN is evident in predicting the PM10 in Saudi Arabia. Hence, this hybrid model can predict the environmental externalities for non-linear and highly noised data. Moreover, the findings can be useful in achieving the sustainable development goal.
The continuous rise in global environmental challenges has led to urgency toward establishing a secure framework to achieve sustainable development goals. This study establishes a novel theoretical framework to analyze the role of energy prices, energy consumption, gold prices and economic growth on environmental degradation in newly industrialized economies. To realize sustainable development goals and foster environmental defence, this study utilizes CS-ARDL as the main econometric approach to investigate the asymmetric association between environmental degradation and relevant factors. We also use AMG, CS-DL, Driscoll-Kray and FGLS to enhance the robustness of our findings. Our econometric approach reveals that energy resource prices and renewable energy consumption reduce environmental degradation, while gold prices and fossil energy consumption elevate environmental pollutants. We also confirm the existence of the EKC hypothesis. The findings of our extensive analysis paved the way for a well-designed environmental policy for NIC economies should focus on renewable energy consumption, green investments, and structural changes.
Carbon mitigation of buildings is critical to promote a net-zero society. The international society has vigorously promoted “Net Zero Carbon Buildings” across the globe, and accounting for building carbon emissions is critical to support this initiative. Embodied carbon, which represents carbon emissions from the entire lifecycle of the buildings, is fundamental for realizing the idea of zero carbon. However, only limited studies have been conducted so far that take into account the city scale. This paper aimed to act as a first try to account for the embodied carbon emissions in buildings in 2020 for the Guangdong-Hong Kong Macau Greater Bay Area in China (GBA). We integrated remote sensing techniques such as night-time light data (NLT) and building material flows analysis to calculate and spatialize the newly generated building material stocks (MS). Based on the MS data, we further applied life cycle assessment (LCA) to assess the embodied carbon in the buildings. The results highlighted that over 163 million tons of embodied carbon in buildings of GBA are expected to be generated, from 497 million tons of newly generated building MS in 2020. The embodied carbon in each life cycle stage is valuable for further lifecycle-based policy designs for: (i) supporting the updating of the green building certification system with consideration of the embodied carbon; (ii) promoting the green building material application and certification; and (iii) reducing the embodied carbon intensity from compact urban planning policy, such as the urban agglomeration policies in GBA. The goal of this paper was to shed a light on reducing carbon emissions from the perspective of the entire lifecycle and promote the development of net zero carbon buildings in China and Asia-Pacific.
The Paleoproterozoic Francevillian Group in the eastern Gabonese Republic has been recently attracting increasing attention because it includes distinctive macroscopic structures interpreted as eukaryotic fossils. Therefore, its depositional setting and associated redox conditions need to be clearly understood. To clarify these, petrological studies and rare earth element (REE) abundances of carbonate rocks of the Francevillian Group in the Lastoursville basin were determined with ICP-MS coupled with a laser ablation sampling technique. Detailed microscopic observations indicate that dolostones in this basin underwent the complicated diagenetic history, including sparitization, decomposition of organic matter, reduction of ferromanganese oxides, silicification, and later calcification. The dolostones of lower Francevillian B (FB) Formation show heterogeneous textures formed by alterations during diagenesis. By the alterations, changes of REE abundances and degrees of positive Y anomalies appear to have occurred. This indicates that REE signatures in carbonate rocks can vary during diagenesis. Characteristically high Mn contents in the dolostones of upper FB Formation were derived from the reduction of ferromanganese oxides in sediments. The signatures of REE abundance in dolostones can be explained by the mixing of seawater and ferromanganese oxide components. In that sense, the dolostones inherited the trace element characteristics of precursor ferromanganese oxides even after the diagenesis. Y/Ho values of the most primary parts of each sample exceed 33, the newly determined threshold value for marine carbonate rocks based on our compilation. The magnitude of the La anomaly values also falls within the range of that of Holocene reefal microbialites. These indicate that a precursor of the dolostone of the Francevillian Group was deposited in a marine environment. In addition, presence of Ce anomalies in the upper FB Formation suggests that the Paleoproterozoic ocean was oxidized to such an extent as for Ce to be preferentially absorbed by ferromanganese oxides.
The global economy relies heavily on oil production, but it is not without challenges. During the period of economic globalization, the revenue and costs associated with oil production have become disjointed, which has had a detrimental effect on the environment. As a result, environmental protection has been compromised, environmental regulations have been weakened, natural resources have been exploited, and climate change has been exacerbated. This study examines the impact of revenue minus the production cost of oil, energy use, and shadow economy on environmental degradation in APEC countries from 1991 to 2020, using the economic globalization index as a moderating variable. The result explains that revenue minus the oil production cost and the shadow economy are negatively associated with environmental degradation. Energy use and economic globalization are positively associated with environmental degradation in APEC countries. Revenue minus the production cost of oil and shadow economy improve the environment. Energy use and economic globalization degrade the environment in APEC countries. Policymakers are encouraged to advance technologies and upgrade infrastructure to reduce greenhouse gas emissions; also, promoting energy-efficient practices in other sectors, like transportation and manufacturing, could contribute to a healthier environment.
When confronted with ecological challenges, trading ecologically friendly products involving renewable technologies, green management practices, and effluent treatment methods could alleviate ecological degradation on a global scale while considering the macroeconomic policy framework. Therefore, this study determines the effectiveness of fiscal and monetary policy instruments in moderating the relationship between green trade openness (i.e., trade in environmentally related products) and ecological sustainability. Applying panel quantile regression on data from 20 OECD members from 2003 to 2016, we found that green trade openness supports ecological sustainability through a gains-from-trade approach. Concerning moderation effects, expenditure-driven fiscal expansion reinforces the favorable influence of green trade openness on ecological sustainability across ecologically less/moderately efficient economies, while it does the reverse for ecologically more efficient members. Taxation-driven fiscal contraction promotes ecological sustainability amelioration impact of green trade openness for economies with below-average ecological quality and remains neutral for those with average/above-average ecological quality. Besides, interest rate-driven monetary contraction proliferates the ecological sustainability enhancement effect of green openness. We suggest that the fiscal and monetary policies demand unambiguous coordination with the OECD’s trade policy structure for optimal environmental outcomes of trading in environmental products. These insights would help OECD’s green trade policies gain momentum to facilitate the attainment of the Climate Action agenda of the United Nations’ Sustainable Development Goals.
Phlogopite solid-solutions have a wide pressure–temperature (P-T) stability field and are ubiquitous in a wide variety of geological settings, from deep lithosphere magmatic environments to upper crust metamorphic domains. Phlogopite composition represents therefore a valuable physical–chemical archive and may provide important information regarding its crystallization and the petrogenesis of the host-rock. In this paper we examine the phlogopite phenocrysts from the well-known Fort Regent mica-bearing lamprophyre minette from St. Helier (Island of Jersey, UK). Phlogopite phenocrystals from lamprophyres generally show normal-step and continuous compositional zoning, however those from the Fort Regent minette show a peculiar texture characterized by dark brown high-Ti (average TiO2 ≈ 8.5 wt.%) cores enveloped by euhedral low- to mid-amplitude zonation due to oscillatory contents in Ti, Fe and Mg. Thermo-barometry modelling based on biotite-only composition yields relatively high P-T estimates (T ≈ 970 ± 54 °C at P ≈ 0.73 ± 0.13 GPa) for cores whereas lower values (T ≈ 790 ± 54 °C at P ≈ 0.29 ± 0.13 GPa) are obtained for the outer rims. Comparable temperatures (T ≈ 1075 ± 54 °C) but extremely high and anomalous pressure values (P ≈ 1.82 ± 0.13 GPa) are obtained for the yellowish inner rims. The combination of electron micro probe (EMP) analysis and single-crystal infra-red (FTIR) imaging in the OH-stretching region shows that the exceptional and oscillatory Ti contents are due to the Ti-vacancy substitution, typical of crystallization and growth processes of HP/HT environments. Raman imaging provides additional insight for this process, confirming the dominant dioctahedral nature for the Ti-Fe-rich cores and outer rims. Interpretation of thermobaric estimates obtained from the phlogopite composition-only model, based on the fine-scale compositional evolution, shows that pressure–temperature values from low-Ti high-Mg domains should be carefully evaluated because the substitution mechanisms during the dark mica growth are not univocally related to pressure–temperature variation of the crystallizing environment. Our results demonstrate how a multidisciplinary approach based on the combination of chemical investigations and vibrational spectroscopies could represent a valuable tool to evaluate pressure–temperature estimates from biotite composition-only thermo-barometry models and therefore to correctly unravel HP/HT petrogenetic processes at a very fine scale.
Geophysical well logs are widely used in geological fields, however, there are considerable incompatibilities existing in solving geological issues using well log data. This review critically fills the gaps between geology and geophysical well logs, as assessed from peer reviewed papers and from the authors’ personal experiences, in the particular goal of solving geological issues using geophysical well logs. The origin and history of geophysical logging are summarized. Next follows a review of the state of knowledge for geophysical well logs in terms of type of specifications, vertical resolution, depth of investigations and demonstrated applications. Then the current status and advances in applications of geophysical well logs in fields of structural geology, sedimentary geology and petroleum geology are discussed. Well logs are used in structural and sedimentary geology in terms of structure detection, in situ stress evaluation, sedimentary characterization, sequence stratigraphy division and fracture prediction. Well logs can also be applied in petroleum geology fields of optimizing sweet spots for hydraulic fracturing in unconventional oil and gas resource. Geophysical well logs are extending their application in other fields of geosciences, and geological issues will be efficiently solved via well logs with the improvements of advanced well log suits. Further work is required in order to improve accuracy and diminish uncertainties by introducing artificial intelligence. This review provides a systematic and clear descriptions of the applications of geophysical well log data along with examples of how the data is displayed and processed for solving geologic problems.
Flash floods (FFs) are amongst the most devastating hazards in arid regions in response to climate change and can cause the loss of agricultural land, human lives and infrastructure. One of the major challenges is the high-intensity rainfall events affecting low-lying areas that are vulnerable to FF. Several works in this field have been conducted using ensemble machine learning models and geohydrological models. However, the current advancement of eXtreme deep learning, which is named eXtreme deep factorisation machine (xDeepFM), for FF susceptibility mapping (FSM) is lacking in the literature. The current study introduces a new model and employs a previously unapplied approach to enhance FSM for capturing the severity of floods. The proposed approach has three main objectives: (i) During- and after-flood effects are assessed through flood detection techniques using Sentinel-1 data. (ii) Flood inventory is updated using remote sensing-based methods. The derived flood effects are implemented in the next step. (iii) An FSM map is generated using an xDeepFM model. Therefore, this study aims to apply xDeepFM to estimate susceptible areas using 13 factors in the emirates of Fujairah, UAE. The performance metrics show a recall of 0.9488), an F1-score of 0.9107), precision of (0.8756) and an overall accuracy of 90.41%. The accuracy of the applied xDeepFM model is compared with that of traditional machine learning models, specifically the deep neural network (78%), support vector machine (85.4%) and random forest (88.75%). Random forest achieves high accuracy, which is due to its strong performance that depends on factors contribution, dataset size and quality, and available computational resources. Comparatively, the xDeepFM model works efficiently for complicated prediction problems having high non-collinearity and huge datasets. The obtained map denotes that the narrow basins, lowland coastal areas and riverbank areas up to 5 km (Fujairah) are highly prone to FF, whilst the alluvial plains in Al Dhaid and hilly regions in Fujairah show low probability. The coastal city areas are bounded by high-rise steep hills and the Gulf of Oman, which can elevate the water levels during heavy rainfall. Four major synchronised influencing factors, namely, rainfall, elevation, drainage density, distance from drainage and geomorphology, account for nearly 50% of the total factors contributing to a very high flood susceptibility. This study offers a platform for planners and decision makers to take timely actions on potential areas in mitigating the effects of FF.
Re-examination of three specimens from the Kanchenjunga Himal of Nepal via in situ Lu-Hf garnet geochronology yields evidence of multiple garnet growth events. Spot analyses from grain cores in two specimens define Paleozoic regressions whereas analyses from grain rims in the same specimens define low-precision regressions consistent with the timing of Himalayan orogenesis. These dates contrast with previously published low dispersion, ca. 290 Ma isotope dissolution (ID) Lu-Hf garnet dates for the same rocks. Modelling of Lu and spot age distribution in representative grains from the specimens examined yields calculated dates that approximate the Permian-age regressions through the original ID data. These findings demonstrate that it is possible to generate low dispersion ID Lu-Hf data from multi-generational garnet with significantly different-age growth events when approximately equal proportions of the different age reservoirs are included in multi-component aliquots.
In the gneiss terrane on the south side of the Eoarchean Isua supracrustal belt, ultramafic rocks with relict abyssal peridotite mineralogy (
The mechanism of gold migration, enrichment, and precipitation in forming world-class gold deposits has been a topic of wide interest, particularly where these deposits are abundant in tellurides. The Jiaodong Peninsula in eastern China hosts some of the world-class gold deposits among which the Jinqingding deposit is one of the best examples with substantial telluride mineralization and thus provides opportunity to investigate the genetic connection between tellurium and gold mineralization. The orebody in this deposit is hosted in the NE-NNE-trending Jiangjunshi-Quhezhuang fault with the Jurassic Kunyushan granitic pluton as wall rock. The deposit involved three mineralization stages as inferred from assemblages and crosscutting relationships between veins. These stages are: (I) pre-ore gold-poor quartz-pyrite veins, (II) main ore auriferous quartz-pyrite-Te/Bi-minerals ± sphalerite ± chalcopyrite ± barite ± marcasite veins, and (III) post-ore quartz-calcite veins. We present here the textural, isotopic, and geochemical variations of different stages/generations of pyrite based on scanning electron microscopy-energy dispersive spectroscopy (SEM–EDS), electron probe microanalysis (EPMA), and laser ablation inductively coupled plasma mass spectrometry (LA–ICP–MS).
The Carboniferous to Permian tectono-sedimentary evolution of the southern Junggar area brings new insights into understanding the subduction-collision processes in the northern Tianshan region. Integrating geophysics, geochemistry, and geochronology approaches, this study investigates the Carboniferous–Permian strata in the southern Junggar Basin. The results have revealed three distinct tectono-stratigraphic evolutionary stages, each marked by a distinctive volcano-sedimentary sequence. The Early Carboniferous strata suggest intense volcanic activities in the southern Junggar area. During the Late Carboniferous, the southern Junggar Basin was controlled by normal faulting in an extensional setting, receiving sedimentary inputs from the Junggar terrane. The Lower Permian, unconformably overlying the Upper Carboniferous, was shaped by an extensional regime and is comprised by volcano-clastic sequences that received detritus from the Yili-Central Tianshan block. These findings indicate that a Late Carboniferous forearc basin developed in the southern Junggar area, and it evolved into a post-collisional rift in the Early Permian. This period marked a dynamic shift from bidirectional subduction (rollback) to the detachment of the North Tianshan oceanic slab. We propose that the collision between the Yili-Central Tianshan block and the Junggar terrane, along with the closure of the North Tianshan Ocean, likely occurred in the Late Carboniferous (ca.306–303 Ma).