查看更多>>摘要：? 2022This article addresses Mexico's present situation in the lithium industry and its near future, ceteris paribus. Mexico's short- and long-term lithium supply will not improve by the exploration and exploitation planned by the nationalistic objectives of the current government. This analysis demonstrates that significant changes must be made to Mexico's energy policy to promote the development of lithium due to five risks: manufacturing capacity, misaligned incentives, industrial policies, geographic concentration, and limited international coordination. Therefore, although the world's largest lithium mine was found in Sonora in 2019, Mexico's policy approaches to nationalize lithium exploration and exploitation will not allow the country to capitalize on the boom of this industry, as happened in Bolivia. In the short term, Mexico's policies will create an exploration deficit due to the country's lack of know-how and investment. Thus, Mexico will not extract lithium in the long term nor benefit from the demand increase and development of a value chain, especially in North America. Given these risks, this article postulates that Mexico's lithium policy should be revised to open its market to foreign investment and use this nascent market to a good advantage.
查看更多>>摘要：? 2022 Elsevier LtdWater has become one of the main causes of social conflict in the mining industry because, due to the scarcity of water, community members and mining companies see one another as competitors for water. To address these concerns, mining companies have formulated strategies to improve their projects in terms of water consumption. The objective of this research is to determine the strategies that companies use to gain social license to operate regarding three different facets of water-centric decision making: water source, water role, and water stewardship. With this end in mind, qualitative case studies and multiple sources of information have been curated and analyzed. The results indicate that to improve the level of social license to operate, the company may use multiple water sources, assign multiple roles for water (in terms of usage), and incorporate multiple social actors into the water stewardship plan.
查看更多>>摘要：? 2022 Elsevier LtdMining for minerals, while a good source of income to the local economies, results in significant environmental damages. The Aravalli Mountain Range in India protects Delhi and adjoining areas from desertification risks posed by the Thar desert through acting as a physical barrier. However, it has become degraded over time due to persistent illegal mining and encroachment by real estate developers. Restoring the Aravallis will not only reduce the threat of desertification but also help generate valuable ecosystem services, such as water supply, to the nearby cities. Yet restoration efforts undertaken thus far have remained largely unsuccessful due to a lack of sufficient financial incentives. This study asks whether financial incentives for afforestation, such as desertification prevention permits and water-based payments for ecosystem services, can promote restoration of mineral rich commons in the Aravallis. It further seeks to explore the role of risk of restoration failure in discouraging conservation effort amongst communities. We design an incentive structure through which local communities benefit from water and land based payments resulting from restoration. We develop a dynamic optimization model of afforestation decisions in presence of land use change related risk from mining and real estate mafia to derive implications for the long run sustainability of the commons. Results indicate that desertification prevention permits and water payments can be useful towards incentivizing communities to take up protection of the degraded lands. However, when presented with the threat of failure of the restoration project due to illegal mineral extraction, community's optimal afforestation level declines considerably.
查看更多>>摘要：? 2022 Elsevier LtdStrategic petroleum reserves are a major too to mitigate the risk oil supply disruptions and to discourage the use of energy as a geopolitical weapon. Moreover, oil supply disruption severely damages the energy security and economic growth as well. Therefore, we used econometric estimation and combine numerous relevant, multidimensional and comprehensive set of indicators through Principal Component Analysis (PCA) to measure the physical oil supply risk and strategic petroleum reserves in order to enhance the social welfare in South Asia. Our analysis proved that 30% of the shortfall in crude oil distribution in markets accounts for the biggest vacillated system of crude oil costing. This shortfall immediately increases forecasted social welfare wastage via a 40% decrease in gross domestic product, which is estimated at $700 in South Asia and $3000 in the largest crude oil economy. The quantity and value of oil supply required for the strategic petroleum reserves to trigger the optimum building and drawdown of the oil are calculated using PCA and game solution. Limited testing of private sector inventory adjustments was less encouraging, suggesting that private actions may have partially offset some government reductions. Therefore, anticipated costs grow at 4% in regular market situations, decrease nearly 8% in interrupted market conditions, and decrease 9% in highly interrupted market conditions. With the benefit of retrospection, improved management may have considerably increased the value of the strategic petroleum reserves, particularly during the peak disruption. A quantitative risk evaluation of crude oil disruptions reveals the importance of crude oil accumulation and the drawdown of vital national crude oil stockpiles as governments seek to optimize consumer well-being while simultaneously maintaining control over oil stockpiles.
查看更多>>摘要：? 2022Considering the complexity, interactions, and dynamics that permeate the Supply Chain (SC), computational modeling and simulation promote determining the system's behavior and decision-making. Among simulation techniques, system dynamics (SD) investigate on recognition of variables and their dynamic behavior trend throughout time in the SC. This paper presents a comprehensive SD model related to upstream steel SC management up to four echelons: concentrate, pellet, sponge iron, and steel. The model noticed Causal-Loops Diagrams (CLD) and Stock-Flow Diagrams (SFD) and provided a simulation framework. The robustness of the proposed model was evaluated by implementing the defined model in a multi-echelon steel complex in Iran. Various scenarios were analyzed applying stochastic simulation to include selected random variables. Iron ore grade and tonnage play the most critical role in the network's performance. With an approximately 4% increase in the iron ore grade, steel production costs decreased 2.4%. The influence of the simultaneous uncertainty in iron ore grade and iron ore supply in the range of extreme levels of actual historical data resulted in an increase and decrease of +14% and ?32% on the total steel production costs, respectively. Furthermore, removing energy subsidies and increasing five times in price results in a rise in total expenses up to 60% and a fall in the marginal profit up to 48%.
查看更多>>摘要：? 2022 Elsevier LtdIn recent years, with the continuous growth of consumer electronic products and the vigorous development of the new energy vehicle industry, the demand for global cobalt resources has increased sharply. The increasing demand is putting enormous pressure on the sustainable development of cobalt resources and exposing them to the risk of supply shortages. At present, most of the researches on cobalt resources are limited to trade between countries or to resource flows within individual countries, and fail to consider the flow of cobalt resources from the international and domestic perspectives as a whole. To observe the cobalt resources in global flows and analyses cobalt resource dependence at the global and national levels, this article systematically combs the various cobalt element forms by using the concept of the industrial chain. Additionally, it constructs a trade-related global cobalt element material flow network model of each phase in the cobalt industrial chain, using the cobalt production data and products containing cobalt trade data in 2016, 2018 and 2020, based on complex network and material flow analysis. This article used network indicators to analyze the important trading countries (regions), and trading hubs for the cobalt. And then it used the Herfindahl-Hirschman Index to measure the concentration of importing and exporting markets in each phase of the cobalt industrial chain. For the important trading countries (regions), this article further calculated the degree of resource dependence between industrial chain phases within each country (region) and classified them accordingly. The results showed that the important trading countries (regions) of cobalt resources have been occupied by specific countries in recent years. Most of the important trade hub countries (regions) for cobalt are the countries (regions) experiencing rapid economic development. The export trade has a higher degree of monopoly than import trade in the cobalt industrial chain. According to the degree of dependence between the industrial chain phases, the important trading countries (regions) can be divided into four categories, the overall dependence of the industrial chain phases in countries (regions) with higher levels of economic development will be deeper. According to the results, some suggestions were put forward for the sustainable development of cobalt in different countries, including extending the industrial chain, establishing and improving national recovery systems.
查看更多>>摘要：? 2022 Elsevier LtdIn this paper we exploit the wavelet analysis approach to investigate oil-food price correlation and its determinants in the domains of time and frequency. Wavelet analysis is able to differentiate high frequency from low frequency movements which correspond, respectively, to short and long run dynamics. We show that the significant local correlation between food and oil is only apparent and this is mainly due both to the activity of commodity index investments and, to a lesser extent, to a growing demand from emerging economies. Moreover, the activity of commodity index investments gives evidence of the overall financialisation process. In addition, we employ wavelet entropy to assess the predictability of the time series under consideration at different frequencies. We find that some variables share a similar predictability structure with food and oil. These variables are the ones that move the most along with oil and food. We also introduce a novel measure, the Cross Wavelet Energy Entropy Measure (CWEEM), based on wavelet transformation and information entropy, with the aim of quantifying the intrinsic predictability of food and oil given demand from emerging economies, commodity index investments, financial stress, and global economic activity. The results show that these dynamics are best predicted by global economic activity at all frequencies and by demand from emerging economies and commodity index investments at high frequencies only.
查看更多>>摘要：? 2022 Elsevier LtdProduction planning and scheduling optimisation for underground mining operations has continued to attract significant attention over the last decades. This has been necessitated by the growing need for operations to meet their shareholder's expectations sustainably under increasingly challenging operational dynamics. Several studies have been undertaken to utilise mathematical programming models such as mixed-integer programming, heuristics and simulation algorithms including combinations of these techniques for production scheduling optimisation with some notable achievements noted in extant literature. However, the limited reach of standalone mathematical optimisation models under increasing volumes of input data spurred by the booming information technology (IT) platforms has become more apparent and pertinent for increased scholarly attention. The growing emergence of big data, driven by the industrial digitisation and automation has seen an increased appetite for data-driven optimisation planning and scheduling largely in manufacturing and operations management. However, the scarcity of discussion in this novel and fast-evolving area in the underground mining space presents a glaring blind spot that appeals for thoughtful conversations to narrow that gap. This paper seeks to discuss opportunities for application of data analytics and machine learning to improve production planning and scheduling efficacy in underground mining. Specific focus will then be narrowed to opportunities for incorporating predictive analytics and machine learning to improve the accuracy of mathematical optimisation models. The overarching intent is to support the attainment of mineral production targets through enabling schedule dynamic response to variability in key determinant variables such as ore grade and tonnages.
查看更多>>摘要：? 2022 Elsevier LtdEven though a few studies have focused on natural resources and commodity sectors by considering the pandemic, they have only compared their status in pre-COVID19 to post-COVID19. None of the studies has directly examined the causal relationship between the pandemic, and natural resource index and the primary commodity-related sector indices. This study fills the gap of exploring the dynamic association between them by analyzing the causal relationship between the COVID19, and natural resources index and the primary commodity-related sectors (i.e., agribusiness, energy, and metals & mining) by applying a novel time-varying causality test on daily data from January 23, 2020, to November 12, 2021. The empirical results support the presence of time-varying causality from COVID19 to natural resources, agribusiness, energy and metals & mining. The results obtained from the rolling window algorithm support causal linkages between the variables however at several points it fails to capture the dynamics of linkages between the variables which is captured by the recursive window algorithm. The outcome is robust when the pandemic is proxied by either number of cases or deaths. Similarly, the findings obtained from heteroskedastic-robust specification also validate our findings. Several policy implications are further discussed in the study.
查看更多>>摘要：? 2022 Elsevier LtdIn this study, the DEA model was selected to evaluate China's regional energy efficiency; then, the Tobit model was used to analyze the impact of green finance on energy efficiency. The following conclusions were drawn: (1) From a national perspective, although the average level of energy efficiency is low, it shows a trend of continuous improvement during the study period. From a regional perspective, the development of energy efficiency in different regions is significantly unbalanced, being highest in the east and lowest in the west. (2) For the whole country, green finance has not significantly forced the improvement in energy efficiency; however, the eastern region's green finance policy has improved energy efficiency, but that of the central region has inhibited energy efficiency. In the west, green finance has almost no relationships with energy efficiency. (3) Considering the country as a whole, the level of marketization has no significant relationships with energy efficiency in the central and western regions but has a promoting effect on energy efficiency in East China. Technological progress has obvious positive effects on energy efficiency in China in the three major regions; foreign direct investment has a negative effect in the western region and positive effect in the eastern and central regions but no effect on the whole country. Energy consumption structure has negative relationships with energy efficiency in all regions; energy prices have a promoting effect in the eastern region and a positive effect in the central and western regions, and no significant impact on the whole country.
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