Violation Cost Research Articles

The deterrent effect of environmental judicature on firms' pollution emissions: Evidence from a quasi-natural experiment in China

This paper investigates the deterrent effect of environmental judicature on firms' pollution emissions. Regarding the establishment of environmental courts (ECs) in China as a quasi-natural experiment, our staggered Difference-in-Differences (DID) estimation shows that strengthening environmental judicature has a negative, causal effect on firms' pollution emissions. The exogenous establishment of ECs decreases industrial firms' pollution emission intensity by approximately 7.8 %. Heterogeneity analysis shows that the negative effect of ECs on pollution emissions is particularly salient for firms in regions with weak environmental awareness, firms in industries with low competition, and firms with a high intensity of pollution emissions. Mechanism analysis indicates that the establishment of ECs enhances environmental justice and increases environmental violation costs, thereby having a deterrent effect on firms' pollution emissions. To comply with the more stringent environmental judicature, firms located in cities with ECs may temporarily cut their production, use more clean energy and buy more desulfurization equipment. In the long term, they may engage in more green and cooperative innovations, further substantiating the deterrent impact of stringent environmental judicature. Our findings can offer valuable insights for corporate green transformation and government pollution control.

Cortical processes of multisensory plausibility modulation of vibrotactile perception in virtual environments in middled-aged and older adults

Digital technologies, such as virtual or augmented reality, can potentially support neurocognitive functions of the aging populations worldwide and complement existing intervention methods. However, aging-related declines in the frontal-parietal network and dopaminergic modulation which progress gradually across the later periods of the adult lifespan may affect the processing of multisensory congruence and expectancy based contextual plausibility. We assessed hemodynamic brain responses while middle-aged and old adults experienced car-riding virtual-reality scenarios where the plausibility of vibrotactile stimulations was manipulated by delivering stimulus intensities that were either congruent or incongruent with the digitalized audio-visual contexts of the respective scenarios. Relative to previous findings observed in young adults, although highly plausible vibrotactile stimulations confirming with contextual expectations also elicited higher brain hemodynamic responses in middle-aged and old adults, this effect was limited to virtual scenarios with extreme expectancy violations. Moreover, individual differences in plausibility-related frontal activity did not correlate with plausibility violation costs in the sensorimotor cortex, indicating less systematic frontal context-based sensory filtering in older ages. These findings have practical implications for advancing digital technologies to support aging societies.

Motion Planning for Autonomous Vehicles in Unanticipated Obstacle Scenarios at Intersections Based on Artificial Potential Field

In unanticipated obstacle scenarios at intersections, the safety and mobility of autonomous vehicles (AVs) are negatively impacted due to the conflict between traffic law compliance and obstacle avoidance. To solve this problem, an obstacle avoidance motion planning algorithm based on artificial potential field (APF) is proposed. An APF-switching logic is utilized to design the motion planning framework. Collision risk and travel delay are quantified as the switching triggers. The intersection traffic laws are digitalized and classified to construct compliance-oriented potential fields. A potential violation cost index (PVCI) is designed according to theories of autonomous driving ethics. The compliance-oriented potential fields are reconfigured according to the PVCI, forming violation cost potential fields. A cost function is designed based on compliance-oriented and violation cost potential fields, integrated with model predictive control (MPC) for trajectory optimization and tracking. The effectiveness of the proposed algorithm is verified through simulation experiments comparing diverse traffic law constraint strategies. The results indicate that the algorithm can help AVs avoid obstacles safely in unanticipated obstacle scenarios at intersections.

Developing a new flexibility-oriented model for generation expansion planning studies of renewable-based energy systems

This paper presents a flexibility-oriented generation expansion planning (GEP) model with the goal of increasing renewable energy penetration. The model incorporates GEP and unit commitment while considering improved reserve requirements. Utilizing GEP and unit commitment results in considering short-term operational constraints. Furthermore, given the increase in renewable portfolio standards (RPS) and the need for accurate reserve requirements determination, it is essential to consider improved reserve requirements in the proposed model. Moreover, this model utilizes a metric that measures power system flexibility deficiency and leads to flexible power system expansion by integrating it into the objective function. The flexibility-oriented GEP model is developed using mixed-integer linear programming and applied to a scaled-sized case study representing the national power system of Iran with a twenty-year planning horizon. The results show that neglecting improved reserve requirements leads to plans short on flexibility, and considering these requirements substantially declines the upward flexibility violation costs by 68% under the base scenario and 87% under increasing non-hydro RPS scenario. Furthermore, using the flexibility metric coupled with improved reserve requirements changes the optimal path for power system expansion and reduces the risk of flexibility deficiency. Also, increasing RPS by 35% would strongly depend on developing fast-response dispatchable generation units in the years before renewable energy resources expansion; however it doesn’t impose a significant extra cost of planning and the total cost will increase just by 9.43%.

A two-stage approach to aircraft recovery under uncertainty

The paper presents a two-stage approach for minimizing the impact of daily disruptions on an airline’s published flight schedule. The problem is characterized by uncertainty in the duration of the disruption and the point in time when its length becomes known. Both a single-commodity network model and multi-commodity network model with side constraints are developed to first determine the flights that are most likely to be affected, and then to adjust their schedules to achieve system-wide optimality. The overall objective is to minimize the weighted sum of total passenger delay costs, cancellation costs, curfew violation costs, and variation from the original schedule. The two types of uncertainty are addressed by examining a range of scenarios that reflect the most likely outcomes. The results provide guidance and a measure of robustness for the flight director as the disruption unfolds. A rolling horizon approach that closely mimics current procedures used by several airlines is also presented to provide a benchmark for comparisons with the two-stage solutions.

Grid‐aware provision and activation of fast and slow power flexibilities from distributed resources in low and medium voltage grids

AbstractThe intermittencies of renewable generations give rise to situations, which require both slow‐ and fast‐ramping flexibility capability from a variety of resources connected at the medium voltage (MV) andlow voltage (LV) distribution grids. Moreover, this may increase the grid reinforcement costs. To defer this reinforcement, the grid needs to be operated optimally. This paper proposes—(a) such an optimal operational methodology forthe MV and LV grids; and (b) an aggregated flexibility estimation methodology separately for fast and slow services at the primary substation (transmission interface). The methodologies based on model‐based MV grids and sensitivity coefficients‐based LV grids are suitable for LV grids where an up‐to‐date, accurate model and topology are not always available. Besides, the costs and resources associated to create full LV grid model and visibility should be prevented. The approaches, here, use the synchronised and accurate measurements from grid monitoring devices located at the LV distribution grids. They have been validated on a real MV and LV networks of a Swiss distribution grid operator equipped with such devices. The results, in terms of reducing technical losses, reducing grid violation costs,and estimating flexibility capability show the efficacy of the proposed methodologies and therefore, can be easily deployed.

Multi-objective genetic algorithm-based wind turbines control

With the development of science and technology, new energy technology has increasingly become a hot spot of academic research. Today, solar, nuclear, tidal and wind power generation is gradually replacing thermal power generation. Aiming at the configuration problem of traditional wind power generation, this research will optimize the control of wind turbine generator sets, so as to realize the configuration optimization of the entire distribution network. In this study, a MOG (MOG) algorithm control optimization method was introduced. First, each scenario is constructed, and components such as centralized wind power plants are configured and controlled through the internal power flow program; then, decision variables are defined, and three objective functions with trade-off relationships are constructed. Finally, the MOG algorithm is used for iterative solution, and the point with the lowest violation cost is selected as the optimal solution. Simulation experiments including three different configuration scenarios were conducted in MATLAB environment for comparing the influence of different connection modes on distribution network. According to experimental results, the effectiveness of the proposed method is verified and it is proved that the proposed method can be applied to complex distribution networks.

MILP Model for Optimal Day-Ahead PDS Scheduling Considering TSO-DSO Interconnection Power Flow Commitment Under Uncertainty

We propose a comprehensive framework for the optimal day-ahead scheduling of power distribution systems (PDS), based on a mixed-integer linear programming (MILP) model. The interaction between transmission system operator (TSO) and distribution system operator (DSO) is considered, where TSO informs DSO of the day-ahead forecast concerning the expected power flow (PF) commitment at the TSO-DSO interconnection points and the violation costs. We propose a MILP model to determine the optimal voltage and power settings of distributed energy resources (DERs), such as dispatchable distributed generators and energy storage units and optimal adjustments of capacitor banks controlled by current. The objective function includes the minimization of energy losses, voltage violations, power curtailment of DERs using volt-watt strategy, and violation of TSO-DSO interconnection PF commitment. Furthermore, we propose a method to estimate the degree of uncertainty in the PF commitment. Therefore, the proposed method can help achieve an optimal operation of the distribution system; in addition, the TSO can best model uncertainty at TSO-DSO interface points and thereby procure reduced amounts of resources to address these uncertainties. Numerical results obtained for a system based on real data highlight the several potential applications of the proposed framework.

Evaluating EIA implementation in China: An empirical study of 161 EIA judicial cases

To play EIA's role as a preventative and participatory tool, the 2003 Environmental Impact Assessment (EIA) Law was significantly amended in 2016 to optimize EIA approval authority and procedure, raise project developers' violation costs, and clarify public participation rules. This article evaluates how the main provisions of this Law have been enforced in practice by analyzing 161 judicial cases collected. Case analysis reveals the main case typology, plaintiffs, violations concerned, and environmental agencies' losing rate. More specifically, the majority of these 161 cases are brought by project developers, challenging the legality of penalty decisions and disapproval decisions imposed on them. The dominant form of violation is construction without EIA approval, a chronic challenge haunting China's EIA enforcement. The interested parties are admitted with standing in most lawsuits filed against EIA approval, mainly due to the relaxed standing requirements. Nevertheless, the losing rate of environmental agencies in selected EIA cases is higher than that of general environmental cases, raising doubts about the effectiveness of the 2016 EIA Law's efforts to promote a more efficient and prudent approval procedure.

Deep reinforcement learning-based two-timescale Volt-VAR control with degradation-aware smart inverters in power distribution systems

Higher penetration of intermittent solar photovoltaic (PV) systems in the distribution grid results in frequent voltage fluctuations. The conventional voltage regulating devices operating on a slow-timescale need to be supplemented with the fast-operating smart inverters with adjustable reactive power setpoints. Complete and accurate information about distribution network topology and line parameters is necessary for conventional model-based Volt-VAR control (VVC) methods. However, such information is often unavailable. To tackle these challenges, a reinforcement learning-based two-timescale VVC algorithm is proposed in this paper that jointly controls the conventional voltage regulating devices at the slow-timescale and the smart inverters at the fast-timescale. Our proposed VVC algorithm simultaneously minimizes voltage violation costs and system operation costs in a model-free manner utilizing historical operational data. Two hierarchically organized agents are set up for the slow-timescale and fast-timescale problems, which are coupled through a communication scheme. The two sets of control policies are learned concurrently by a deep deterministic policy gradient and multi-agent soft actor-critic algorithm respectively. Comprehensive numerical studies performed with the IEEE 123-bus distribution test feeder show that the proposed framework can identify near optimal control actions of voltage regulating devices and smart inverters in real-time operations.

Lagrangian Heuristic for Multi-Depot Technician Planning of Product Distribution and Installation with a Lunch Break

In this paper, we consider a technician planning scheme stemming from product distribution and installation in a manufacturing enterprise that considers factors such as soft time windows, skill areas, lunch breaks, and outsourcing options, among others. The goal is to identify the optimal partition of technicians into groups and assignment of customers to technician groups and find the optimal routes for technician groups to minimize the sum of the travel cost, soft time window violation cost, and outsourcing cost. To address this problem, the study develops a tailored Lagrangian heuristic that incorporates several strategies to speed up convergence and produce sharper bounds. Computational comparisons between the developed heuristic and MIP solver are presented. The results reveal that the bounds found by the developed algorithm outperform those found by CPLEX for large instances, and it is capable of identifying high-quality feasible solutions to large-scale problems.

Congruence-based contextual plausibility modulates cortical activity during vibrotactile perception in virtual multisensory environments

How congruence cues and congruence-based expectations may together shape perception in virtual reality (VR) still need to be unravelled. We linked the concept of plausibility used in VR research with congruence-based modulation by assessing brain responses while participants experienced vehicle riding experiences in VR scenarios. Perceptual plausibility was manipulated by sensory congruence, with multisensory stimulations confirming with common expectations of road scenes being plausible. We hypothesized that plausible scenarios would elicit greater cortical responses. The results showed that: (i) vibrotactile stimulations at expected intensities, given embedded audio-visual information, engaged greater cortical activities in frontal and sensorimotor regions; (ii) weaker plausible stimulations resulted in greater responses in the sensorimotor cortex than stronger but implausible stimulations; (iii) frontal activities under plausible scenarios negatively correlated with plausibility violation costs in the sensorimotor cortex. These results potentially indicate frontal regulation of sensory processing and extend previous evidence of contextual modulation to the tactile sense.

Chance-constrained model predictive control-based operation management of more-electric aircraft using energy storage systems under uncertainty

On more electric aircraft (MEA), reducing fuel consumption and guaranteeing flight safety are pursued by efficient operational management of the electrical power system (EPS). Considering the growing number of onboard electric loads and the increasing complexity of EPS architecture due to the integration of multiple power converters and energy storage systems (ESSs), system-level operation control is required to manage power distribution, load scheduling, and ESSs. In this paper, a chance-constrained stochastic model predictive control (CC-SMPC) method is proposed to improve both the system operation in terms of the system's cost and reconfiguration activities as well as the ability to cope with uncertainties due to fluctuating load demands. Both normal and faulty operating conditions are investigated with multi-failure cases, resulting in different uncertainty propagation paths. The system's operational and technical requirements are formulated as a set of deterministic and probabilistic constraints in the CC-SMPC model. To verify the effectiveness of the proposed strategy, a comprehensive comparison study is conducted. Two uncertainty/failure cases are taken into account and simulations are performed for both offline and online control strategies while the Monte-Carlo algorithm is used for scenario generation. The results are evaluated using the proposed evaluation framework, showing that the CC-SMPC achieves better performance compared to deterministic MPC (DMPC) in both cases. In an offline testing framework, comparing the performance of DMPC and CC-SMPC strategies shows that CC-SMPC reduces the power constraint violations for batteries and generators in all cases following the selected confidence level. In addition, in an online testing framework with 1 % violation probability, the following results are observed in the two cases: In the EPS normal condition, CC-SMPC reduces the total cost by 31.4 % and the overall constraint violation cost by 93 %; while in the EPS faulty condition, CC-SMPC reduces the total cost by 4.37 %, and the overall constraint violation cost by 96 %.

Hybrid heuristic algorithm for cost-efficient QoS aware task scheduling in fog–cloud environment

Fog cloud computing promotes the combination of fog and cloud nodes to satisfy the requests of data accessing from IoT (Internet of Things) devices. To reduce the data delay and enhancing the QoS (Quality of Service), effective task scheduling is one of the major requirements in fog cloud environment. Numerous approaches have been deployed by the researchers for maintaining the QoS requirements. However, the emergence of bursty traffic affects the process of task scheduling due to high service latency. Effective QoS is promoted by minimizing the costs of computation, communication and deadline violation cost. To achieve the main objective of cost minimization, the proposed work is implemented by adopting a novel model called HFSGA (Hybrid Flamingo Search with a Genetic Algorithm) for better task scheduling. Seven basic benchmark optimization test functions are used to compare the performance of HFSGA with other well-known algorithms. Also, Friedman Rank Test is performed to demonstrate the significance of the results. The implemented model presents better outcomes with respect to PDST (Percentage of deadline satisfied task), makespan and cost. When compared to the existing algorithms that include Ant colony optimization (ACO), Particle swarm optimization (PSO), Genetic Algorithm (GA), Min-CCV, Min-V and Round Robin (RR) approach, the proposed work shows better output in satisfying the task scheduling process.

Scheduling Optimization of Offshore Oil Spill Cleaning Materials Considering Multiple Accident Sites and Multiple Oil Types

Coastal governments have been preventing and controlling pollution in the marine environment by enhancing the construction of hardware and software facilities. The dispatch of offshore oil spill cleaning materials must be upgraded and optimized to cope with repeated offshore oil leak incidents while simultaneously improving cleaning efficiency and the ability to resist oil spill hazards. Accordingly, we set up a multiobjective optimization model with time window constraints to solve the scheduling optimization problem of offshore oil spill accidents with multiple locations and oil types. This model integrates the minimal sum of fixed costs, fuel consumption costs, maximum load violation costs, and time window penalty costs to solve the scheduling optimization problem of an offshore oil spill accident. An improved genetic algorithm is designed to solve the proposed mathematical model effectively and to make a scientific decontaminated decision-scheduling scheme. The practicality of the model and algorithm is validated by using a specific instance, demonstrating that the suggested method can effectively solve the schedule optimization problem for cleaning materials.

Could regulatory information disclosure impact the behaviour of corporate over-financialization? Evidence from Chinese comment letters

ABSTRACT Taking Chinese A-share listed firms from 2014 to 2018 as our samples, this paper investigates the impact of regulatory information disclosure on corporate over-financialisation behaviour from comment letters. The empirical results show that financialisation-related comment letters can effectively reduce the behaviour of corporate over-financialisation, and this positive governance effect is more pronounced in companies with strong market arbitrage motivation. Further tests show that the more intensity of the financialisation-related comment letters, the higher the governance effect of comment letters on over-financialisation. Specifically, the governance effect of inquiries mechanism is more significant when the financialisation-related comment letters have the questions of “over-financialisation’. From the specific influence mechanism, the governance effect of financialisation-related comment letters is achieved by potential violation cost and the pressure of market attention. Finally, the governance effect of financialisation-related comment letters on over-financialisation can spill over to non-comment-letter-receivers in the same industry or in the same corporation groups.

Environmental courts and foreign direct investments: evidence from China.

As China's pollution problems worsen, environmental disputes are increasing rapidly. However, only 1% of environmental disputes can be resolved via judicial channels. Based on the establishment of environmental courts since 2007, we employ the multi-period DID approach to investigate the impact of environmental courts on foreign direct investments. We find that (1) compared with cities without environmental courts, FDI of cities with environmental courts would drop by 3.32% from the average, which is consistent with the pollution haven hypothesis. Besides, we verify the credibility of the conclusion through detailed endogeneity discussions, placebo tests, and robustness tests. (2) The negative effect of environmental courts on foreign direct investments is more salient in the east, regions with higher historical environmental investment, and regions with more serious local protectionism. (3) There are two channels. The greater risks of environmental litigation and stricter environmental public supervision brought by environmental courts would lead to additional environmental violation costs, thereby inhibiting foreign direct investments. Our findings provide new causal evidence for the pollution haven hypothesis from the perspective of intensive environmental justice.

An Efficient Homotopy Method for Solving the Post-Contingency Optimal Power Flow to Global Optimality

Optimal power flow (OPF) is a fundamental problem in power systems analysis for determining the steady-state operating point of a power network that minimizes the generation cost. In anticipation of component failures, such as transmission line or generator outages, it is also important to find optimal corrective actions for the power flow distribution over the network. The problem of finding these post-contingency solutions to the OPF problem is challenging due to the nonconvexity of the power flow equations and the large number of contingency cases in practice. In this paper, we introduce a homotopy method to solve for the post-contingency actions, which involves a series of intermediate optimization problems that gradually transform the original OPF problem into each contingency-OPF problem. We show that given a global solution to the original OPF problem, a global solution to the contingency problem can be obtained using this homotopy method, under some conditions. With simulations on Polish and other European networks, we demonstrate that the effectiveness of the proposed homotopy method is dependent on the choice of the homotopy path and that homotopy yields an improved solution in many cases. For at least 5% of the test cases, bad local minima were identified, and the homotopy method yielded a solution that was significantly better than state-of-the-art interior point methods in terms of reducing the violation cost during a catastrophic contingency scenario.

Efficient Sensitivity-Based Cooperation Concept for Hierarchical Multilayer Process Automation of Steam-Powered Plants

Hierarchical optimization architectures are typically employed to manage industrial steam processes efficiently. The key challenge today is to find a near global optimum despite these subdivided automation structures. This paper proposes a novel cooperation concept between hierarchical layers. In the upper layer, optimal static setpoints are computed for economic operation schedules of the entire plant. In the lower layer, a model predictive controller realizes these schedules, regulates the dynamic plant parts, and treats occurring disturbances optimally in a stochastic manner. To adequately overcome limitations due to mismatches between the optimization layers (caused by model mismatches or disturbances), it is essential to establish an efficient cooperation concept. Therefore, the presented concept exchanges specific sensitivity information between the optimization layers, where it is skillfully exploited to the benefit of global automation objectives, resulting in optimal expected operating costs. The novel concept is demonstrated via simulation studies calibrated with industrial measurements of a chipboard manufacturer. A performance analysis shows that the proposed cooperation concept outperforms alternative approaches, leading to the best possible trade-off between additional fuel and steam demand violation costs.

Can anti-corruption improve the quality of environmental information disclosure?

This study takes the anti-corruption campaign of the 18th CPC National Congress in China as an exogenous impact build a double difference (DID) model, proving that anti-corruption can increase the quality of enterprise environmental information disclosure significantly. After a series of tests such as parallel trend test and placebo test, the results are still robust. The intermediary effect model results indicate that the anti-corruption increases the violation cost of enterprises and encourages enterprises to carry out innovation activities, and ultimately increase the quality of enterprise environmental information disclosure. Heterogeneity analysis finds that non-state-owned enterprises, large enterprises, and regions with more substantial environmental supervision will improve the quality of enterprise environmental information disclosure to a greater extent. The findings of this paper can help provide policy inspiration for improving the mandatory environmental information disclosure system and strengthening the enforcement of environmental regulations.