Abstract The mechanism of the environmental regulations affecting carbon emissions is complex, and for many years it has been a hot research topic for researchers. However, few of the previous studies have focused on whether or not environmental regulations can potentially influence carbon emissions through technical efficiency. Therefore, the present study chose to investigate the mediation effects of the technical efficiencies between environmental regulations and carbon emissions using the provincial panel data of energy-intensive industries for the period ranging from 2005 to 2015. The results were as follows: (i) In regard to the entire group of energy-intensive industries, it was found that environmental regulations could not only potentially directly reduce carbon emissions, but also indirectly reduce carbon emissions through technical efficiency. In other words, technical efficiency had played a partial mediation role between the environmental regulations and the carbon emissions; (ii) It was observed that for the different subdivided energy-intensive industries, the different levels of technical efficiency had shown various mediation effects. For example, the mediation effects of technical efficiency in the petroleum processing and coking (PPC) industry; papermaking and paper products (PPP) industry, and raw chemical materials and chemical products (RCMCP) industry, were found to be similar to those of the whole energy-intensive industries. Furthermore, the “Porter Hypothesis” had been verified in the nonmetal mineral products (NMP) industry. However, there were no mediation effects observed in the smelting and pressing of ferrous metals (SPFM) and smelting and pressing of nonferrous metals (SPNM) industries; (iii) In this study, in accordance with the regression results of the double-threshold models, it was found that for the energy-intensive industries as a whole, the carbon emission reduction effects of environmental regulations had become stronger with the increases in technical efficiency. For the PPP and RCMCP industries, there was an inverted U-shaped relationship observed between environmental regulations and carbon emissions when the technical efficiency was taken as the threshold variable. Therefore, based on the research conclusions achieved in this study, some policy recommendations were put forward.
Andrew Calabrese-Day, Emilie LaVoie-Ingram, Kathryn Ream
et al.
Solar, supernova, and atmospheric neutrinos, and possibly weakly interacting massive particle (WIMP) dark matter, have been interacting in the Earth beneath our feet for billions of years. The ''paleo-detector'' technique seeks to detect and characterize the induced crystalline defects from these events, in particular from energetic nuclear recoils, which in some minerals can be preserved on these timescales. Such defects can manifest as nuclear recoil tracks, on the order of a few nanometers wide and extending up to hundreds of microns in length, which can be detected with nanoscale-resolution microscopy. In order to test the feasibility of the paleo-detector technique and to study the formation and morphology of track defects in promising mineral candidates like olivine, we use ion irradiation to artificially implant tracks to effectively mimic astrophysical particle interactions. We present a study of heavy-ion track width as a function of depth, which we relate to ion energy, in an olivine crystal irradiated with 15 MeV Au$^{+5}$ using scanning transmission electron microscopy (STEM). Unlike previous studies, which measure tracks at the surface of the irradiated sample, we instead take measurements at various target depths via focused ion-beam sectioning of the irradiated sample. No etching techniques are used to enhance the tracks. In addition, we provide a comparison to predictions from simulations using SRIM software and previous results with a variety of ion species and energies. Notably, we find that a significant change in track continuity across the energy range studied (0.4-12.9 MeV) is indicative of the transition between electronic and nuclear stopping power dominance, consistent with the simulations' predictions. Overall, the tracks produced in olivine indicate that this mineral is an attractive candidate for paleo-detection, with robust track creation at the MeV scale.
Sequential pattern mining (SPM) with gap constraints (or repetitive SPM or tandem repeat discovery in bioinformatics) can find frequent repetitive subsequences satisfying gap constraints, which are called positive sequential patterns with gap constraints (PSPGs). However, classical SPM with gap constraints cannot find the frequent missing items in the PSPGs. To tackle this issue, this paper explores negative sequential patterns with gap constraints (NSPGs). We propose an efficient NSPG-Miner algorithm that can mine both frequent PSPGs and NSPGs simultaneously. To effectively reduce candidate patterns, we propose a pattern join strategy with negative patterns which can generate both positive and negative candidate patterns at the same time. To calculate the support (frequency of occurrence) of a pattern in each sequence, we explore a NegPair algorithm that employs a key-value pair array structure to deal with the gap constraints and the negative items simultaneously and can avoid redundant rescanning of the original sequence, thus improving the efficiency of the algorithm. To report the performance of NSPG-Miner, 11 competitive algorithms and 11 datasets are employed. The experimental results not only validate the effectiveness of the strategies adopted by NSPG-Miner, but also verify that NSPG-Miner can discover more valuable information than the state-of-the-art algorithms. Algorithms and datasets can be downloaded from https://github.com/wuc567/Pattern-Mining/tree/master/NSPG-Miner.
To understand how fluctuations arise and are distributed in international trade, a question crucial for economic risk assessment and policymaking, we analyze strong adverse fluctuations-collapsed trades-defined as individual trades with sharp annual volume declines. Adopting a hypergraph framework for a fine-scale trade-centric representation of international trade, we find that collapsed trades (hyperedges) are clustered and their occurrence decays algebraically with trade volume (weight), which suggests inhomogeneous, epidemic-like spreading of collapse in the international trade hypergraph. Modeling collapse propagation as a contagion process and analyzing its dynamics, we show that a positive degree-weight correlation and a volume-decaying collapse rate synergistically suppress the onset of global collective collapse. Notably, the degree-weight correlation persisted but the volume-decay of the collapse rate weakened during the 2008-2009 global economic recession, resulting in a broader collapse spread. Our study shows how the interplay between structure and dynamics stabilizes complex systems.
As machine learning models are increasingly embedded into society through high-stakes decision-making, selecting the right algorithm for a given task, audience, and sector presents a critical challenge, particularly in the context of fairness. Traditional assessments of model fairness have often framed fairness as an objective mathematical property, treating model selection as an optimization problem under idealized informational conditions. This overlooks model multiplicity as a consideration--that multiple models can deliver similar performance while exhibiting different fairness characteristics. Legal scholars have engaged this challenge through the concept of Less Discriminatory Algorithms (LDAs), which frames model selection as a civil rights obligation. In real-world deployment, this normative challenge is bounded by constraints on fairness experimentation, e.g., regulatory standards, institutional priorities, and resource capacity. Against these considerations, the paper revisits Lee and Floridi (2021)'s relational fairness approach using updated 2021 Home Mortgage Disclosure Act (HMDA) data, and proposes an expansion of the scope of the LDA search process. We argue that extending the LDA search horizontally, considering fairness across model families themselves, provides a lightweight complement, or alternative, to within-model hyperparameter optimization, when operationalizing fairness in non-experimental, resource constrained settings. Fairness metrics alone offer useful, but insufficient signals to accurately evaluate candidate LDAs. Rather, by using a horizontal LDA search approach with the relational trade-off framework, we demonstrate a responsible minimum viable LDA search on real-world lending outcomes. Organizations can modify this approach to systematically compare, evaluate, and select LDAs that optimize fairness and accuracy in a sector-based contextualized manner.
Nicolas Apfel, Holger Breinlich, Nick Green
et al.
Gravity equations are often used to evaluate counterfactual trade policy scenarios, such as the effect of regional trade agreements on trade flows. In this paper, we argue that the suitability of gravity equations for this purpose crucially depends on their out-of-sample predictive power. We propose a methodology that compares different versions of the gravity equation, both among themselves and with machine learning-based forecast methods such as random forests and neural networks. We find that the 3-way gravity model is difficult to beat in terms of out-of-sample average predictive performance, especially if a flexible specification is used. This result further justifies its place as the predominant tool for applied trade policy analysis. However, when the goal is to predict individual bilateral trade flows, the 3-way model can be outperformed by an ensemble machine learning method.
Groundwater contamination through potentially harmful metals (PHMs) is an environmental hazard in Pakistan with significant human health risk reports. The current research was conducted in Sheikhupura District, which is a major industrial site in Punjab, Pakistan. According to the Punjab Directorate of Industries in Pakistan, there are a total of 748 industries in this area. These industries produce a lot of waste and effluent, which contaminate the environment with harmful and toxic materials. Continuous irrigation with industrial effluent and sewage sludge may make groundwater sources vulnerable. Therefore, we collected 243 groundwater samples from community tube wells to investigate the groundwater quality cconcerning PHM contaminations in the study area. This research presents the values of pH, total dissolved solids (TDS), electrical conductivity (EC), and potentially harmful metals (PHMs) like arsenic (As), manganese (Mn), lead (Pb), zinc (Zn), copper (Cu), nickel (Ni), and iron (Fe). PHMs such as As (91%), Mn (14%), Pb (97%), Fe (45%), Zn (15%), in these samples were beyond the permitted limit recommended by the world health organization (WHO). Principal component analysis (PCA) results with total variability of (60%) reveal that the groundwater sources of the study area are contaminated about 30.9, 31.3, and 37.6% of contaminations of groundwater sources of this study are resulted from geogenic sources, anthropogenic sources, or both geogenic and anthropogenic sources, respectively. Such sources may include rock-water interaction, mining actions, agricultural practices, domestic sewage, and industrial effluent in the study area. Saturation indices show that the aquifers of the study area are saturated with lead hydroxide, zinc hydroxide, and goethite minerals, indicating that these minerals have a vital role in the contamination of groundwater. Health risk assessment results predicted that the non-carcinogenic risk (HQ) values of PHMs were found within the permissible limit (<1), except As (1.58E+00) for children, while carcinogenic risk (CR) values of all selected PHMs were lower than the maximum threshold CR value (1 × 10−4).
Currently, batteries are used as a promising power source for all industries, and are an essential technology field not only for the industry but also for everyday life. Secondary batteries can be charged or discharged within the allowed range, so they can be used continuously for the life of the battery, and the demand for secondary batteries capable of semi-permanent charging/discharging is increasing through the development of technologies such as IoT, AI, and electric vehicles. In particular, the global electric vehicle conversion speed is very fast, and through this, the spent battery reuse/recycling industry is rapidly growing. The supply chain of the battery market consists of raw materials, materials/parts, products, consumers, and recycling. Along with the increase in demand, the field of waste battery recycling is attracting attention with carbon neutrality, and technology development is being carried out in detail by being divided into areas such as reuse, re-manufacturing, and recycling. Among them, the spent battery reuse/recycling market is expected to grow 19 times from $10.77 billion in 2023 to $208.94 billion in 2040, and battery recycling is emerging as a way to secure a stable supply chain for battery metals as the rapid occurrence of spent batteries is expected. In addition, it is important to strengthen the eco-friendliness of the battery industry due to strengthening global carbon neutrality policies and visualizing climate trade regulations. As the demand for key minerals such as lithium and graphite increase and competition to secure global key minerals is in full swing due to changes in industrial paradigms such as decarbonization/electrification, it is also very important to secure competitiveness in securing key minerals. Resource recovery technology for waste batteries is required to respond to supply chain issues and ESG issues, and the importance of recycling waste batteries is increasing. The waste battery supply market is being formed mainly in the United States, China, and Europe, and among them, the United States and Europe are expected to inevitably switch to an eco-friendly battery reuse/recycling market in terms of intensive environmental regulations and supply chain stabilization. Therefore, it is very necessary to develop next-generation technologies related to the recycling of spent batteries. The waste battery recycling process can be defined as a technology that extracts expensive rare metals from the anode active material of waste batteries. Currently, research and development are being conducted centering on lithium cobalt oxide, a small lithium secondary battery, but it is gradually changing centering on nickel cobalt manganese, a medium and large secondary battery for electric vehicles. The rare metal recovery process is divided into a process of removing and crushing the risk of explosion of a waste battery, which is a pre-treatment process, and recovering the rare metal using a chemical solution, which is a post-treatment process. The post-treatment process has higher technical difficulty, uses a solvent extraction method, and a technology that recovers rare metals such as cobalt and nickel and purifies them to a purity of 99.9% or higher is applied while repeatedly performing the solvent extraction method and electrolytic refining. Advanced technology is being developed to improve the performance of post-treatment technology around the world. Technologies such as reducing the production cost of cathode materials, achieving more than 99% recovery of core metals, and reducing wastewater discharge in spent battery reuse/recycling technologies need to be studied intensively. It is expected to secure a stable rare metal through the recycling of waste batteries, and it will be very important to secure battery resource technology through this.
This is the third paper in a series concerning the game-theoretic aspects of position-building while in competition. The first paper set forth foundations and laid out the essential goal, which is to minimize implementation costs in light of how other traders are likely to trade. The majority of results in that paper center on the two traders in competition and equilibrium results are presented. The second paper, introduces computational methods based on Fourier Series which allows the introduction of a broad range of constraints into the optimal strategies derived. The current paper returns to the unconstrained case and provides a complete solution to finding equilibrium strategies in competition and handles completely arbitrary situations. As a result we present a detailed analysis of the value (or not) of trade centralization and we show that firms who naively centralize trades do not generally benefit and sometimes, in fact, lose. On the other hand, firms that strategically centralize their trades generally will be able to benefit.
We have designed an innovative portfolio rebalancing mechanism termed the Cascading Waterfall Round Robin Mechanism. This algorithmic approach recommends an ideal size and number of trades for each asset during the periodic rebalancing process, factoring in the gas fee and slippage. The essence of the model we have created gives indications regarding whether trades should be made on individual assets depending on the uncertainty in the micro - asset level characteristics - and macro - aggregate market factors - environments. In the hyper-volatile crypto market, our approach to daily rebalancing will benefit from volatility. Price movements will cause our algorithm to buy assets that drop in prices and sell as they soar. In fact, the buying and selling happen only when certain boundaries are crossed in order to weed out any market noise and ensure sound trade execution. We have provided several numerical examples to illustrate the steps - including the calculation of several intermediate variables - of our rebalancing mechanism. The Algorithm we have developed can be easily applied outside blockchain to investment funds across all asset classes at any trading frequency and rebalancing duration. Shakespeare As A Crypto Trader: To Trade Or Not To Trade, that is the Question, Whether an Optimizer can Yield the Answer, Against the Spikes and Crashes of Markets Gone Wild, To Quench One's Thirst before Liquidity Runs Dry, Or Wait till the Tide of Momentum turns Mild.
The institutionalization of digital trade is one of the most important directions in the formation of the information society in the Russian Federation. The studies reflect the emerging lag in the Russian economy readiness index to support online shopping. The author analyzes the reasons for the lag in the context of the institutional features of the development of digital trade. As the main obstacle that reduces the economic efficiency and competitiveness of digital trade, insufficient attention of the state to the formation of innovative institutions of the digital market is highlighted.
The industrial Internet of Things (IIoT) and network slicing (NS) paradigms have been envisioned as key enablers for flexible and intelligent manufacturing in the industry 4.0, where a myriad of interconnected machines, sensors, and devices of diversified quality of service (QoS) requirements coexist. To optimize network resource usage, stakeholders in the IIoT network are encouraged to take pragmatic steps towards resource sharing. However, resource sharing is only attractive if the entities involved are able to settle on a fair exchange of resource for remuneration in a win-win situation. In this paper, we design an economic model that analyzes the multilateral strategic trading interactions between sliced tenants in IIoT networks. We formulate the resource pricing and purchasing problem of the seller and buyer tenants as a cooperative Stackelberg game. Particularly, the cooperative game enforces collaboration among the buyer tenants by coalition formation in order to strengthen their position in resource price negotiations as opposed to acting individually, while the Stackelberg game determines the optimal policy optimization of the seller tenants and buyer tenant coalitions. To achieve a Stackelberg equilibrium (SE), a multi-agent deep reinforcement learning (MADRL) method is developed to make flexible pricing and purchasing decisions without prior knowledge of the environment. Simulation results and analysis prove that the proposed method achieves convergence and is superior to other baselines, in terms of utility maximization.
Bilateral trade is one of the most natural and important forms of economic interaction: A seller has a single, indivisible item for sale, and a buyer is potentially interested. The two parties typically have different, privately known valuations for the item, and ideally, they would like to trade if the buyer values the item more than the seller. The celebrated impossibility result by Myerson and Satterthwaite shows that any mechanism for this setting must violate at least one important desideratum. In this paper, we investigate a richer paradigm of bilateral trade, with many self-interested buyers and sellers on both sides of a single trade who cannot be excluded from the trade. We show that this allows for more positive results. In fact, we establish a dichotomy in the possibility of trading efficiently. If in expectation, the buyers value the item more, we can achieve efficiency in the limit. If this is not the case, then efficiency cannot be achieved in general. En route, we characterize trading mechanisms that encourage truth-telling, which may be of independent interest. We also evaluate our trading mechanisms experimentally, and the experiments align with our theoretical results.
Intra-firm trade describes the trade between affiliated firms and is increasingly important as global production is fragmented. However, statistics and data on global intra-firm trade patterns are widely unavailable. This study proposes a novel multilevel approach combining firm and country level data to construct a set of country intra-firm trade networks for various segments of the automotive production chain. A multilevel network is constructed with a network of international trade at the macro level, a firm ownership network at the micro level and a firm-country affiliation network linking the two, at the meso level. A motif detection approach is used to filter these networks to extract potential intra-firm trade ties between countries, where the motif (or substructure) is two countries linked by trade, each affiliated with a firm, and these two firms linked by ownership. The motif detection is used to extract potential country level intra-firm trade ties. An Exponential Random Graph Model (ERGM) is applied to the country level intra-firm trade networks, one for each segment of the automotive production chain, to inform on the determinants of intra-firm trade at the country level.
Chengyuan Han, Malte Schröder, Dirk Witthaut
et al.
Understanding the structure and formation of networks is a central topic in complexity science. Economic networks are formed by decisions of individual agents and thus not properly described by established random graph models. In this article, we establish a model for the emergence of trade networks that is based on rational decisions of individual agents. The model incorporates key drivers for the emergence of trade, comparative advantage and economic scale effects, but also the heterogeneity of agents and the transportation or transaction costs. Numerical simulations show three macroscopically different regimes of the emerging trade networks. Depending on the specific transportation costs and the heterogeneity of individual preferences, we find centralized production with a star-like trade network, distributed production with all-to-all trading or local production and no trade. Using methods from statistical mechanics, we provide an analytic theory of the transitions between these regimes and estimates for critical parameters values.
The importance of fertilizers to agricultural production is undeniable, and most economies rely on international trade for fertilizer use. The stability of fertilizer trade networks is fundamental to food security. We use three valid methods to measure the temporal stability of the overall network and different functional sub-networks of the three fertilizer nutrients N, P and K from 1990 to 2018. The international N, P and K trade systems all have a trend of increasing stability with the process of globalization. The large-weight sub-network has relatively high stability, but is more likely to be impacted by extreme events. The small-weight sub-network is less stable, but has a strong self-healing ability and is less affected by shocks. Overall, all the three fertilizer trade networks exhibit a stable core with restorable periphery. The overall network stability of the three fertilizers is close, but the K trade has a significantly higher stability in the core part, and the N trade is the most stable in the non-core part.
Célestin Coquidé, José Lages, Leonardo Ermann
et al.
Using the United Nations Comtrade database, we perform the Google matrix analysis of the multiproduct World Trade Network (WTN) for the years 2018-2020 comprising the emergence of the COVID-19 as a global pandemic. The applied algorithms -- the PageRank, the CheiRank and the reduced Google matrix -- take into account the multiplicity of the WTN links providing new insights on the international trade comparing to the usual import-export analysis. These algorithms establish new rankings and trade balances of countries and products considering every countries on equal grounds, independently of their wealth, and every products on the basis of their relative exchanged volumes. In comparison with the pre-COVID-19 period, significant changes in these metrics occur for the year 2020 highlighting a major rewiring of the international trade flows induced by the COVID-19 pandemic crisis. We define a new PageRank-CheiRank product trade balance, either export or import oriented, which is significantly perturbed by the pandemic.
Abstract Manganese is a necessary and irreplaceable metal resource for the steelmaking industry. The essentiality and non-substitutability make the vulnerability to supply restriction of manganese nonnegligible. With this concern, this study was conducted to present a global trade-linked material flow analysis (MFA) model of manganese for the year of 2017 to provide deep insight into its flow pattern. Using the MFA model, the global manganese flow in 305 categories of commodities within 249 countries was mapped. The results showed that 23.9 million tons (Mt) of manganese were supplied and used in 2017. An estimated 27.9 Mt of manganese contained in various commodities was traded between countries, 38% of which was contributed by the top ten trade flows. China, South Africa, Australia, Brazil, Gabon, the USA, India, Japan and Germany were the nine most important members of the global manganese trade flow community. The lack of a system for the complete recovery of Mn from end-of-life products and slag, and a trend towards increasing geographic concentration of Mn ore supplies, were identified as two areas of potential risk and concern. Corresponding policy recommendations are proposed to resolve these concerns. A more integrated recovery system of manganese needs to be considered, particularly for the developing lithium-ion battery applications. Establishment of national reserve needs to be put on the agenda for the large manganese consumers. High supply concentration of manganese refining and manufacturing stage, which is dominated by China, may need to be changed when prospective global manganese consumption structure changes.