Resources Policy最新文献

This paper provides key insights into China's sustainable energy investment from 1995 to 2021 through a detailed quantitative analysis. It identifies a significant negative correlation between vulnerability to the resource curse and sustainable energy investment, underscoring the detrimental effects of resource dependency on sustainability and energy security. In contrast, innovation, as reflected in patent applications, positively impacts sustainable energy investments, highlighting the role of technological progress. Trademarks show no significant effect on these investments, possibly due to their broader economic role. The study also points out the negative impact of poverty on sustainable energy investment, revealing underlying socioeconomic challenges. The paper recommends strategies for diversification, fostering innovation, alleviating poverty, and investing in renewable energy infrastructure and digital technology to support China's shift towards a sustainable energy future.

This research investigates the intricate interplay between major global mining entities and sustainability initiatives in the Middle East and North Africa (MENA) region, as a key part of Global South countries. The study conducts a comprehensive analysis of the global mining industry at five distinct levels: mining, mining cap-weighted, mining coal exclusion, global gold mining tracker, and global electrification minerals mining. The objective is to comprehend the multi-fractal patterns and implications of their interconnectedness with sustainability and conventional investments. By employing a time-varying parameter vector autoregressive (TVP-VAR) approach to identify the degree of nexus between each pair consisting of a mining index and sustainability or conventional investments, and utilizing the generalized Hurst exponent of a multiscale multi-fractal analysis (MMA) from September 18, 2017, to July 3, 2023, the study reveals that none of the bi-variate interconnectedness adheres to a random walk process, thereby confirming their inefficient behavior. The level of efficiency among pairs varies, demonstrating heterogeneity across the connections. The analysis indicates a relatively lower level of inefficiency in pairs of pairwise connectedness indices (PCIs) related to sustainable investments compared to conventional investments. Generally, conventional investments exhibit more distinct trends or predictability over various time scales, suggesting a less complex and unpredictable pattern compared to sustainable investments. Additionally, the persistence level in PCIs linked to conventional sustainable investments is higher than that of sustainable investments, implying that conventional investments have more predictable associations with mining consortiums. These findings provide valuable insights into the dynamic relationships between global mining entities and sustainability initiatives in the MENA region, with implications for investors, policymakers, and industry stakeholders. The clarity observed in connectivity patterns with conventional investments presents strategic opportunities for investors, potentially influencing regulatory adjustments for sustainable resource governance. The identified inefficiencies create opportunities for refining risk management tools and optimizing investment strategies. Furthermore, the study highlights the stability of connections between conventional sustainable investments and mining consortiums over time, providing guidance for long-term planning and risk mitigation.

The mineral resources trade is significant because these minerals are crucial in developing green and clean energy equipment and plants, which is key to achieving sustainable development targets. However, no past studies have focused on finding the determinants of trade in mineral resources. This analysis fills the gap by analyzing how digital government and digital financial inclusion affect mineral resources trade using the two-stage least squares (2SLS) and generalized method of moments (GMM) estimation techniques. The findings indicate that digital government and financial inclusion promote mineral trade. The interaction between digital government and digital financial inclusion also helps increase the trade of mineral resources. In addition, logistic performance, GDP, and foreign direct investment all play a significant role in boosting trade in mineral resources, while the energy security risks and consumer price index hinder the trade in mineral resources. These findings suggest that policymakers should increase the role of digitalization in the administrative and financial setups to fasten the trade in mineral resources.

Natural resource wealth can contribute to human and economic development if the revenues from natural resource sectors are effectively invested by the government. In particular, countries with abundant natural resources have the potential to experience significant development and improvements in their quality of life. This study first examines the impact of natural resource rents on life expectancy in sub-Saharan Africa. We then investigate the moderating role of the financial sector in this relationship. Using mainly the Generalized Moments Method in a panel of 44 Sub-Saharan African countries over the period 1990–2021, the results obtained in this paper reveal a negative effect of natural resource rents on life expectancy, supporting the resource curse-health hypothesis. However, the stability of the financial system moderates this relationship and makes it positive, at specific thresholds. These results are consistent with Hirschman's conjecture that production leakage is low in landlocked countries, but that there are stronger links with public revenues than with other sectors of activity. The ‘wealth channel’ lubricated by the financial sector that this study identifies calls for greater caution when adopting non-rentier policies in countries exploiting their natural wealth, specifically countries with low human capital. We suggest that a portion of resources should be allocated to financing human capital in order to increase life expectancy.

Growth divergence in resource-rich countries extends the debate over the resource curse hypothesis. The present study examines the relationship between natural resources and economic growth and its transmission channel, with panel data from 95 countries and between 1970 and 2017. It applies two different models such as Panel CS-ARDL and System-GMM to obtain a robust result of the relationship between natural resources and economic growth. The novelty of this study lies in the fact that it attempts to explain the growth divergence in resource-rich countries by examining the impact of natural resources on economic growth and its transmission channels for each per capita income group. CS-ARDL model does not confirm the results from system-GMM, which indicates the existence of resource curse for the panel of all countries. The other results from CS-ARDL support the evidence for the resource curse only in the low income countries and the blessing hypothesis in the developed ones. Low investment and poor total factor productivity are shown to be among the transmission channel of the negative impact of natural resource on economic growth in the developing country.

Mineral extraction involves distinct stages, including drilling, blasting, loading, transporting, and processing minerals at a designated facility. The initial phase is drilling and blasting, crucial for controlled dimensions of crushed stone suitable for the processing plant. Incorrect blasting can lead to unsuitable stone grading and destructive outcomes like ground vibrations, stone projection, air blasts, and recoil. Predicting and optimizing blasting costs (BC) is essential to achieve desired particle size reduction while mitigating adverse blasting consequences. BC varies with rock hardness, blasting techniques, and patterns. This study presents a BC prediction model using data from 6 Iranian limestone mines, employing firefly (FF) and gray wolf optimization (GWO) algorithms. With 146 data points and parameters like hole diameter (D), ANFO (AN), sub-drilling (J), uniaxial compressive strength (σ_c), burden (B), hole number (N), umolite (EM),spacing (S), specific gravity (γr), stemming (T), hole length (H), rock hardness (HA), and electric detonators (Det), the data was split into 80% for model construction and 20% for validation. Using statistical indicators, the model showed good performance, offering engineers, researchers, and mining professionals high accuracy. The @RISK software conducted sensitivity analysis, revealing T parameter as the most influential input factor, where minor T changes significantly affected BC. Lastly, the @RISK software was employed to conduct a sensitivity analysis on the model's outputs. The analyses demonstrated that, among the input factors, the T parameter had the most pronounced effect on the model's output. Even small changes in the value of T led to considerable fluctuations in the predicted BC.

Managers evaluating investment decisions in mining projects have reported using the net present value approach (NPV). However, NPV has the drawback of collapsing variations from uncertainties in future project cash flows into a fixed expected project value today. Ignoring future uncertainties and contingencies can lead managers to make incorrect up-front binary decisions, such as investing in or abandoning the project now. The real options analysis approach (ROA) instead captures variations from uncertainties as volatilities in project value and allows flexibility in investment decisions to be contingent on information as it is revealed. Managers we consulted agreed that ROA is superior to NPV in principle, but too complex to apply in practice. The advanced mathematics and restrictive assumptions required by analytical ROAs make them especially impractical for real-world projects exposed to multiple uncertainties. Furthermore, real-world projects are often multistage and involve valuing a compound option, which is an option on an underlying option, for sequential investment decisions. Extant numerical compound ROAs have been applied to such projects but suffer from various drawbacks. Amongst other issues, they combine variations from multiple uncertainties into a consolidated volatility of project value. This conceals the impact of each source of uncertainty, resulting in inaccurate project valuation and incorrect investment decisions. We present an innovative compound multiple volatility real options approach (C-MVR) to value a multistage project while accommodating separate volatilities of project value arising from multiple uncertainties for making sequential investment decisions. The resultant value is termed the compound enhanced net present value. Implementing C-MVR for a real coal mining project demonstrates how other ROAs can be seen as inaccurate simplifications that produce erroneous investment decisions. C-MVR provides a rigorous and versatile approach that can be applied to many investment decisions across various industries.

Understanding intricate dynamics between rare earth elements (REEs) and green economy, as well as the pivotal role of trade policy uncertainty (TPU), provides significant insights for the concurrent development of these markets. This paper estimates the short- and long-term correlations between the REEs market and green economy by the DCC-MIDAS model. Additionally, it employs the TVP-VAR model to investigate the impact of China’s and U.S. TPU shocks on the long-term correlation. Furthermore, this paper calculates ΔCoVaR to quantify the risk transmission from the REEs market to green economy and explore the impact of TPU shocks on it. The empirical results reveal a positive medium effect of TPU shocks, highlight the heterogeneity of effects of China’s and Due to the devastating effects of global warming and environmental degradation, coupled with heightened environmental awareness, there is an escalating imperative for the transition towards a more sustainable and greener development. These findings provide significant implications for investment decisions, strategic reserves and policymaking within the REEs and green economy sectors in the context of increasing TPU.

In the era of the digital economy, the significance of green technological innovation for enterprises' sustainable development is pivotal. Effectively utilizing redundant resources is crucial to enterprises' sustainable development and innovation. Given this, we explore the impact of the digital economy and redundant resources on enterprises' green technology innovation. It uses a double fixed-effects model on the enterprise-level data of the Shanghai and Shenzhen stock exchanges from 2013 to 2021. The study shows that developing the digital economy can significantly enhance enterprises' green technology innovation. The effect is more obvious in high-tech enterprises, non-heavily polluted enterprises, enterprises in the eastern region, and non-state-owned enterprises. It also test the mediating effect of redundant resources and find that the digital economy can help enterprises exploit unabsorbed redundant resources, absorbed redundant resources, and organizational redundant resources, reduce the negative impacts of redundant resources on enterprises, and enhance their level of green technological innovation. It offer valuable policy suggestions.