Increase Of Renewable Energy Sources Research Articles

Learning-based model predictive secondary frequency control of PV-ESS-EV microgrid

Microgrids comprising photovoltaic (PV), energy-storage systems and electric vehicles have received increasing attention. However, the increase in renewable energy sources, such as PVs, will introduce uncertainty factors to the frequency response, resulting in frequency fluctuations that can reduce power quality. Hence, we propose an outstanding learning-based control approach that combines a model of physical principles with a Gaussian Process (GP) learning approach. The learning model corrects the physical principles model and estimates its uncertainty. Simulations are performed by comparing the proposed controller with conventional model-predictive controllers. The results indicate that the learning-based controller proposed can adjust the frequency with less overshooting and a lower root mean square error compared to the conventional MPC control method.

Virtual Power Plant’s Optimal Scheduling Strategy in Day-Ahead and Balancing Markets Considering Reserve Provision Model of Energy Storage System

In recent years, with the rapid increase in renewable energy sources (RESs), a Virtual Power Plant (VPP) concept has been developed to integrate many small-scale RESs, energy storage systems (ESSs), and customers into a unified agent in the electricity market. Optimal coordination among resources within the VPP will overcome their disadvantages and enable them to participate in both energy and balancing markets. This study considers a VPP as an active agent in reserve provision with an upward reserve capacity contract pre-signed in the balancing capacity (BC) market. Based on the BC contract’s requirements and the forecasted data of RESs and demand, a two-stage stochastic optimization model is presented to determine the VPP’s optimal scheduling in the day-ahead (DA) and balancing energy (BE) markets. The probability of reserve activation in the BE market is considered in this model. The ESS’s reserve provision model is proposed so as not to affect its schedule in the DA market. The proposed optimal scheduling model is applied to a test VPP system; then, the effects of the BC contract and the probability of reserve activation on the VPP’s trading schedule are analyzed. The results show that the proposed model has practical significance.

Sizing and experimental validation of a selfsync droop-based grid forming inverter with direct voltage control

Currently, the transient stability of the grid is mainly ensured by the inertial behavior of its synchronous generators. The increase of renewable energy sources connected to the grid through grid following (GFL) inverters is severely affecting the inertia of the grid and its stability is beginning to be compromised. Moreover, in case of a large disturbance, these renewable energy sources disconnect from the grid by design, thus exacerbating the problem. The idea of mimicking the behavior of synchronous generators in an inverter and participating in the grid stability gave birth to the so-called grid forming (GFM) inverters. This implies a change in paradigm from current controlled sources (GFL) to voltage-controlled sources (GFM). Despite known stability issues in current controlled converters, a typical approach to GFM converters with LCL output filters is based on cascaded current and voltage control loops. An internal current loop controls the inverter’s output currents and an external voltage loop controls the filter capacitors’ voltages. The problem is that this solution is complex and is also marginally stable. Alternatively, in this paper we explore a direct voltage control, which is based on droop control. The proposed control’s output is used for the direct control of the inverter’s output voltage in amplitude and phase. A complete analysis is performed, and a method is proposed to determine the appropriate control parameters as well as the sizing of the inverter components. Finally, an experimental setup is presented and the effectiveness of the proposed method is shown in simulations as well as in real-life experiments.

Congestion forecast framework based on probabilistic power flow and machine learning for smart distribution grids

The increase in renewable energy sources and new technologies such as electric vehicles and storage can generate uncertainties in distribution grid operations, increasing the likelihood of congestions in power lines. Distribution system operators (DSOs) face several challenges while operating their grids in such conditions. These congestions deteriorate the electrical equipment in the long term, reducing its life span. This work proposes a framework to predict grid asset congestions on a daily basis. A congestion forecast framework is proposed by combining probabilistic power flows and machine learning algorithms to support DSOs in their daily decision-making. The framework is tested on a modified IEEE-33 bus system and CINELDI MV Reference system with hourly synthetic data. The results showed that the framework is able to closely predict the congestions on the lines. Computational capabilities are reported and discussed. The study indicates that the framework is a suitable tool for day-to-day congestion predictions in smart distribution grids yielding low error in expected values.

Technical Impacts of Virtual Clean Hydrogen Plants: Promoting Energy Balance and Resolving Transmission Congestion Challenges

This paper presents the VCHP platform as a solution to address PV curtailment and line congestion in scenarios of increasing renewable energy penetration. Solar PV generation profiles and load profiles were generated for three scenarios (2025, 2030, and 2035) using data provided by KPX. Modifications were made to the IEEE 30 Bus model to accurately reflect the Korean power system, including the introduction of PCA and LCA at relevant buses. Line congestion was evaluated using metrics such as TUR, STUR, and TLR. The research findings indicate that integrating the VCHP platform in all scenarios effectively alleviates line congestion, as shown by the TUR remaining below 1. Importantly, the reduction in line losses exceeds the decrease in power flow, demonstrating the effectiveness of VCHP in reducing power losses. The results suggest that as renewable energy sources increase, line congestion issues may arise in the existing power system. However, integrating the proposed VCHP platform is a valuable solution for effectively utilizing surplus PV energy and improving the stability of the power grid. The adoption of such a platform can significantly enhance the operation and management of the power system.

Na-seawater battery technology integration with renewable energies: The case study of Sardinia Island

Europe has committed to net zero carbon dioxide emissions by 2050 to boost the clean energy transition. Renewable electricity will be the key energy medium for decarbonization and a huge increase in renewable energy sources (RES) exploitation is expected. Due to RES stochastic character, an extensive energy storage integration in the energy system is needed to avoid the mismatch between generation and demand profiles.Reactive metals are promising energy carriers and storage media characterized by high volumetric energy densities and circularity, due to ease of storage and transportation, material availability and low cost. Among them, sodium is a largely available element since it can be extracted from seawater and exploited through the innovative sodium-seawater battery (SWB). Sodium cations are transferred from SWB’s open cathode to the anode side during charging. Upon discharge, Na metal is oxidized to Na+ ions, which are discarded in seawater.This study assesses the impact of SWB technology focusing on Sardinia Island as a case study. For short-term application, SWB integration to wave energy converters allows a potential reduction of greater than 85% of generated power fluctuations, largely improving the quality of power injected into the grid. Regarding the long-term scenario, SWBs implementation in the energy system allows coverage of the Sardinia annual energy demand thanks to the integration of ∼340,000 cubic meter of Na metal, corresponding to a 12-m height Na reservoir under 4 soccer fields. SWB application to Sardinia also produces CO2 sequestration while covering ∼29% of desalinated water requirements for the Sardinian population.

Characterization of Instrument Transformers under Realistic Conditions: Impact of Single and Combined Influence Quantities on Their Wideband Behavior.

Instrument transformers (ITs) play a key role in electrical power systems, facilitating the accurate monitoring and measurement of electrical quantities. They are essential for measurement, protection, and metering in transmission and distribution grids and accurately reducing the grid voltage and current for low-voltage input instrumentation. With the increase in renewable energy sources, electronic converters, and electric vehicles connected to power grids, ITs now face challenging distorted conditions that differ from the nominal ones. The study presented in this paper is a collaborative work between national metrology institutes and universities that analyzes IT performance in measuring distorted voltages and currents in medium-voltage grids under realistic conditions. Both current and voltage measuring transformers are examined, considering influence quantities like the temperature, mechanical vibration, burden, adjacent phases, and proximity effects. The study provides detailed insights into measurement setups and procedures, and it quantifies potential errors arising from IT behavior in measuring distorted signals in the presence of the various considered influence quantities and their combinations. The main findings reveal that the temperature has the most evident impact on the inductive voltage transformer performance, as well as the burden, causing significant changes in ratio error and phase displacement at the lower temperatures. As for low-power ITs, establishing a priori the effects of adjacent phases and proximity on the frequency responses of low-power ITs is a complex matter, because of their different characteristics and construction solutions.

Marginal fixed premium for clean hydrogen portfolio standard (CHPS) considering techno-economic analysis of clean hydrogen production based on hydropower plants

As the hydrogen economy recently gains momentum, the Korean government is actively encouraging Independent Power Producers (IPPs) to leverage hydrogen energy. The surging uncertainties in the power market, precipitated by the rapid increase in renewable energy sources (RES), necessitates IPPs to formulate a new benefit structure that utilizes green hydrogen. However, the current economic challenges associated with the operation of RES linked to water electrolysis result in diminished participation from IPPs. In response, the Korean government is planning to implement the Clean Hydrogen Portfolio Standard (CHPS), which requires clean hydrogen power generation and offers corresponding Fixed Premium (FP) subsidies. In this paper, we incrementally increase the FP for RES linked to electrolyzers (ELZ) and derive a marginal fixed premium (FPmar). This method initiates a shift towards higher economic feasibility compared to the traditional benefit structure of power trading-based RES. We suggest a green hydrogen-based business model (BM) that synergizes hydropower with water electrolysis and calculate a suitable CHPS subsidy to ensure the economic feasibility of this BM. In conclusion, this paper equips IPPs operating hydropower with the capability to evaluate the adequacy of the government-provided FP for the economical operation of a hydropower plant linked to water electrolysis. Furthermore, it provides policymakers with the framework to make informed decisions regarding the necessary FPmar to stimulate IPPs’ participation.

The impact of renewable energy on economic growth in the Southern African Power Pool (SAPP)

ABSTRACT The past decade has seen an increase in renewable energy sources in the energy mix due to global environmental and supply security concerns. This paper aimed to investigate renewable energy effects on Gross Domestic Product (GDP) in the Southern African Power Pool (SAPP). The investigation is from 1988 to 2018, using the autoregressive distributed lag (ARDL), Nonlinear ARDL (NARDL), and fully modified OLS (FMOL) techniques. Based on empirical analyses of ARDL and FMOLS, it was found that both renewable and nonrenewable energies have a positive impact on economic growth in the Southern African power pool. However, the NARDL estimation indicates that neither renewable nor nonrenewable energy has a significant effect on economic growth in Southern African power pool. Therefore, based on the findings from both ARDL and FMOLS, the growth hypothesis is supported regarding the connection between renewable energy and economic growth in the Southern Africa power pool. Centered on the results, some policy implications were drawn for SAPP for sustainable economic growth, which includes measures to increase renewable energy in the energy mix, and also to encourage the optimization of cross-border connections where energy production from countries within SAPP that are not restricted by natural resources can be relied on to export sustainable energy. Additionally, building a more sustainable integrated power system in the region will assist SAPP with sustainable energy access and reduce the dependence on nonrenewable energy within the interconnected countries soon.

Offering strategy of a price-maker virtual power plant in the day-ahead market

With the rapid increase of renewable energy sources (RESs), the virtual power plant model (VPP) has been developed to integrate RESs, energy storage systems (ESSs), and local customers to overcome the RESs’ disadvantages. When the VPP’s capacity is large enough, it can participate in the electricity market as a price-maker instead of a price-taker to obtain a higher profit. This study proposes a bi-level optimization model to determine the optimal trading strategies of a price-maker VPP in the day-ahead (DA) market. The operation schedule of the components in the VPP is also optimized to achieve the highest profit for the VPP. In the bi-level optimization problem, the upper-level model is maximizing the VPP’s profit while the lower-level model is the DA market-clearing problem. The bi-level optimization problem is formulated as a Mathematical Problem with Equilibrium Constraints (MPEC), reformulated to a Mixed Integer Linear Problem (MILP), then solved by GAMS and CPLEX. This study applies the bi-level optimization model to a test VPP system, including wind plants (WP), solar plants (PV), biogas energy plants (BG), ESSs, and several customers. The maximum power outputs of WP and PV are 100MW and 90MW, respectively. The total installed capacity of BG is 70MW, while the ESS’ rated capacity is 100MWh. The local customers have the highest total consumption of 100MW. In addition to the VPP, four GENCOs and three retailers participate in the DA market. The results show that the market-clearing price varies depending on the participants’ production/consumption quantity and offering/bidding price. However, based on the optimization model, the VPP can take full advantage of WP and PV available power output, choose the right time to operate BG, then obtain the highest profit. The results also show that with the ESS’ rated capacity of 100MWh, the ESS’ rated discharging/charging power increased from 10MW to 50MW will increase VPP’s profit from 45987$ to 49464$. The obtained results show that the proposed model has practical significance

A GIS Open-Data Co-Simulation Platform for Photovoltaic Integration in Residential Urban Areas

• Advanced co-simulation of rooftop PV production and households electricity demand. • Urban Energy Systems design and RES integration in the context of future smart cities. • Enabling considerations on Demand Side Management and Demand Response strategies. • High spatio-temporal resolution (25 cm and 10 minutes respectively) • Integration in a GIS environment of open-data and models. The rising awareness of environmental issues and the increase of renewable energy sources (RES) has led to a shift in energy production toward RES, such as photovoltaic (PV) systems, and toward a distributed generation (DG) model of energy production that requires systems in which energy is generated, stored, and consumed locally. In this work, we present a methodology that integrates geographic information system (GIS)-based PV potential assessment procedures with models for the estimation of both energy generation and consumption profiles. In particular, we have created an innovative infrastructure that co-simulates PV integration on building rooftops together with an analysis of households’ electricity demand. Our model relies on high spatiotemporal resolution and considers both shadowing effects and real-sky conditions for solar radiation estimation. It integrates methodologies to estimate energy demand with a high temporal resolution, accounting for realistic populations with realistic consumption profiles. Such a solution enables concrete recommendations to be drawn in order to promote an understanding of urban energy systems and the integration of RES in the context of future smart cities. The proposed methodology is tested and validated within the municipality of Turin, Italy. For the whole municipality, we estimate both the electricity absorbed from the residential sector (simulating a realistic population) and the electrical energy that could be produced by installing PV systems on buildings’ rooftops (considering two different scenarios, with the former using only the rooftops of residential buildings and the latter using all available rooftops). The capabilities of the platform are explored through an in-depth analysis of the obtained results. Generated power and energy profiles are presented, emphasizing the flexibility of the resolution of the spatial and temporal results. Additional energy indicators are presented for the self-consumption of produced energy and the avoidance of CO 2 emissions.

HVDC 운영 전략에 따른 제주 전력 계통의 출력제어 및 운영비용 분석

The proportion of renewable power generation in power systems is rapidly increasing, resulting in frequent imbalances. This paper focuses on the impact of renewable energy penetration on power system stability in Jeju, South Korea, under the CFI 2030 policy. With the increase in renewable energy sources, power variability has also increased due to their intermittent nature, leading to supply and demand imbalances and system stability problems. Traditional methods of maintaining power balance, such as curtailment, result in cost and environmental losses for system operators. To address this issue, the paper proposes the use of HVDC power reversal transmission strategies to return excess power from Jeju to the mainland power system. The study analyzes the impact of three operational strategies of HVDC on unit commitment results, considering cost and environmental factors. The results suggest that using HVDC can mitigate curtailment and reduce oversupply, leading to improved power system stability in Jeju island.

Intelligent Controller Design and Fault Prediction Using Machine Learning Model

In a solar power plant, a solid phase transformer and an optimization coordinated controller are utilized to improve transient responsiveness. Transient stability issues in a contemporary electrical power system represent one of the difficult tasks for an electrical engineer due to the rise in uncertain renewable energy sources (RESs) as a result of the need for green energy. The potential for terminal voltage to be adversely impacted by this greater RES raises the possibility of electrical device damage. It is possible to use a solid state transformer (SST) or smart transformer to address a transient response issue. These devices are frequently employed to interact between RES and a power grid. SST features a variety of regulated converters to maintain the necessary voltage levels. This method can therefore simultaneously lessen power fluctuations and transient responsiveness. In order to improve the quality of RES power injections and the electrical system’s transient stability, this work provides a controller design for a solar photovoltaic (SPV) system that is connected to the grid by SST. The optimization of a controller model is proposed by modifying a PI controller taken from a commercial one. With the use of IEEE 39 standard buses, the proposed controller is tested. When evaluating the effectiveness of a suggested controller, it is important to take into account a variety of solar radiation patterns as well as a time delay uncertainty that can range from 425 ms to 525 ms. According to simulation results, the proposed controller can be employed to lessen power fluctuation brought on by unpredictable RES. Additionally, the proposed coordinated regulation of SPV and SST can prevent catastrophic damage in the event of substantial disturbances like a circuit breaker collapsing to expand a power line due to a fault by inhibiting significant voltage cycles within an electronic appliance’s rated voltage limit. The results indicate that a transitory stability issue in a modern power system caused by an unforeseen increase in RES may be addressed utilizing the suggested controllers as alternatives.

OpenEdgePMU: An Open PMU Architecture with Edge Processing for Future Resilient Smart Grids

The increase in renewable energy sources (RESs) in distribution grids is a major driver for achieving green energy goals worldwide. However, RES power inverters affect power quality, increase power losses, and, in certain cases, may cause power interruptions due to harmonics, deterioration of the rate of change of frequency, and inability to rapidly react in grid faults. Today, phasor measurement units (PMUs) are the ultimate tools for real-time monitoring of distribution grids’ health, and they enable several data-driven added-value services such as fast and automated fault detection, isolation, and recovery; state estimation; power quality monitoring; dynamic events analysis, etc. The present paper proposes an open hardware and software PMU platform, which is low cost, high performance, expandable, and, in general, suitable for research and innovation activities. The system is based on two processor modules (a digital signal processor from Texas Instruments TMS320c5517, and a microprocessor System-in-Package from Octavo Systems OSD3358), two local databases of 64 Gbytes each, GPS module, 5G modem interface, as well as analog and signal conditioning circuits to interface three-phase power voltage and current signals. The entire hardware design, schematics, and instrumentation components, as well as all firmware and software functions are completely open source. Pilot operation of the prototype design has been installed in three medium-/low-voltage substations in Cyprus, as well as twelve substations in Spain and Italy.

Can the new energy vehicles (NEVs) and power battery industry help China to meet the carbon neutrality goal before 2060?

China is working to boost the manufacture, market share, sales, and use of NEVs to replace fuel vehicles in transportation sector to get carbon reduction target by 2060. In this research, using Simapro life cycle assessment software and Eco-invent database, the market share, carbon footprint, and life cycle analysis of fuel vehicles, NEVs, and batteries were calculated from the last five years to next 25 years, with a focus on the sustainable development. Results indicate globally, China had 293.98 m vehicles and 45.22% worldwide highest market share, followed by Germany with 224.97 m and 42.22% shares. Annually China's NEVs production rate is 50%, and sales account for 35%, while the carbon footprint will account for 5.2 E+07 to 4.89 E+07 kgCO2e by 2021–2035. The power battery production 219.7 GWh reaches 150%–163.4%, whereas carbon footprint values in production and use stage of 1 kWh of LFP 44.0 kgCO2eq, NCM-146.8 kgCO2eq, and NCA-370 kgCO2eq. The single carbon footprint of LFP is smallest at about 5.52 E+09, while NCM is highest at 1.84 E+10. Thus, using NEVs, and LFP batteries will reduce carbon emissions by 56.33%–103.14% and 56.33% or 0.64 Gt to 0.006 Gt by 2060. LCA analysis of NEVs and batteries at manufacturing and using stages quantified the environmental impact ranked from highest to lowest as ADP > AP > GWP > EP > POCP > ODP. ADP(e) and ADP(f) at manufacturing stage account for 14.7%, while other components account for 83.3% during the use stage. Conclusive findings are higher sales and use of NEVs, LFP, and reduction in coal-fired power generation from 70.92% to 50%, and increase in renewable energy sources in electricity generation expectedly will reduce carbon footprint by 31% and environmental impact on acid rain, ozone depletion, and photochemical smog. Finally, to achieve carbon neutrality in China, the NEVs industry must be supported by incentive policies, financial aid, technological improvements, and research and development. This would improve NEV's supply, demand, and environmental impact.

Analytical uncertainty propagation for multi-period stochastic optimal power flow

The increase in renewable energy sources (RESs), like wind or solar power, results in growinguncertainty also in transmission grids. This affects grid stability through fluctuating energy supplyand an increased probability of overloaded lines. One key strategy to cope with this uncertainty isthe use of distributed energy storage systems (ESSs). In order to securely operate power systemscontaining renewables and use storage, optimization models are needed that both handle uncertaintyand apply ESSs. This paper introduces a compact dynamic stochastic chance-constrained DC optimalpower flow (CC-OPF) model, that minimizes generation costs and includes distributed ESSs. AssumingGaussian uncertainty, we use affine policies to obtain a tractable, analytically exact reformulation asa second-order cone problem (SOCP). We test the new model on five different IEEE networks withvarying sizes of 5, 39, 57, 118 and 300 nodes and include complexity analysis. The results showthat the model is computationally efficient and robust with respect to constraint violation risk. Thedistributed energy storage system leads to more stable operation with flattened generation profiles.Storage absorbed RES uncertainty, and reduced generation cost.

초전도 아크유도형 DC 차단기의 유도침 설계를 위한 방법론

As renewable energy sources increase, DC transmission and distribution systems are attracting attention. Renewable energy sources are exposed to accidents such as fire, so a DC breaker must be used safely. However, the previously invented DC circuit breakers have a short mechanical life. This research team proposed a superconducting arc-induction type DC circuit breaker to solve this problem. The superconducting arc-induction type DC circuit breaker comprises DC power, normal load, induction needle, and mechanical breaking contact. This paper proposes considerations when designing an induction needle, which is the most important element of a superconducting arc induction type DC circuit breaker. For this purpose, the interrupting time according to the curvature radius and location of the induction needle was analyzed. As a result, when the radius of curvature of the induction needle was 0.2 mm, the position of the induction needle was optimal at 4.7 mm on the x-axis and 11.05 mm on the y-axis.

Renewable Energy Pathways toward Accelerating Hydrogen Fuel Production: Evidence from Global Hydrogen Modeling

Fossil fuel consumption has triggered worries about energy security and climate change; this has promoted hydrogen as a viable option to aid in decarbonizing global energy systems. Hydrogen could substitute for fossil fuels in the future due to the economic, political, and environmental concerns related to energy production using fossil fuels. However, currently, the majority of hydrogen is produced using fossil fuels, particularly natural gas, which is not a renewable source of energy. It is therefore crucial to increase the efforts to produce hydrogen from renewable sources, rather from the existing fossil-based approaches. Thus, this study investigates how renewable energy can accelerate the production of hydrogen fuel in the future under three hydrogen economy-related energy regimes, including nuclear restrictions, hydrogen, and city gas blending, and in the scenarios which consider the geographic distribution of carbon reduction targets. A random effects regression model has been utilized, employing panel data from a global energy system which optimizes for cost and carbon targets. The results of this study demonstrate that an increase in renewable energy sources has the potential to significantly accelerate the growth of future hydrogen production under all the considered policy regimes. The policy implications of this paper suggest that promoting renewable energy investments in line with a fairer allocation of carbon reduction efforts will help to ensure a future hydrogen economy which engenders a sustainable, low carbon society.

A Review of Microgrid Energy Management Strategies from the Energy Trilemma Perspective

The energy sector is undergoing a paradigm shift among all the stages, from generation to the consumer end. The affordable, flexible, secure supply–demand balance due to an increase in renewable energy sources (RESs) penetration, technological advancements in monitoring and control, and the active nature of distribution system components have led to the development of microgrid (MG) energy systems. The intermittency and uncertainty of RES, as well as the controllable nature of MG components such as different types of energy generation sources, energy storage systems, electric vehicles, heating, and cooling systems are required to deploy efficient energy management systems (EMSs). Multi-agent systems (MASs) and model predictive control (MPC) approaches have been widely used in recent studies and have characteristics that address most of the EMS challenges. The advantages of these methods are due to the independent characteristics and nature of MAS, the predictive nature of MPC, and their ability to provide affordable, flexible, and secure MG operation. Therefore, for the first time, this state-of-the-art review presents a classification of the MG control and optimization methods, their objectives, and help in understanding the MG operational and EMS challenges from the perspective of the energy trilemma (flexibility, affordability, and security). The control and optimization architectures achievable with MAS and MPC methods predominantly identified and discussed. Furthermore, future research recommendations in MG-EMS in terms of energy trilemma associated with MAS, MPC methods, stability, resiliency, scalability improvements, and algorithm developments are presented to benefit the research community.

Does green finance development goals affects renewable energy in China

Methodsfor reducing GHG emissions and the challenges of climate change, such as “green finance”, have emerged as an approach that combines financial services and products that confront a wider range of sustainability issues, such as commercial pollution reduction, environmental management, improved sanitation, and environmental conservation. Therefore, our study aimed to investigate how green financing and renewable energy sources in China contribute to sustainable development. This study employs a unique methodology called panel cointegration and causality modelling to analyse the factors that led to the growth of renewable energy in China between 2005 and 2020. The results demonstrate the value of green finance development and private sector participation in localised and worldwide growth. Trademark registrations will increase by 0.487% and financial development filings by 0.144% for every 1% increase in renewable energy sources. When private sector participation is enhanced by 0.032%, trademarks and patents increase by 0.057%. Investment, trade, and human development calm these relationships. Several robustness tests support our findings. The study's empirical evidence, policymakers and environmentalists now have helpful knowledge for creating and implementing environmental projects with long-term financial advantages.