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The major crises that hit the world economy in the last 15 years caused a growing presence of the state in the economy. Deep crises almost inevitably give rise to growing public interventions to avoid the collapse of the economic and social system. However, the demand for more state interventions does not make it less important to be concerned about the extension and quality of these interventions in order to contain their possible negative effects on the economy as a whole. The paper is an attempt at dealing with these issues by using a simple growth model inspired by Domar's contributions. The main result is that state interventions can produce positive effects on the economy by not simply increasing aggregate demand but, most of all, by contributing to raise the productivity and the rate of growth of the economy. An objective that can be realized by mainly devoting public outlays to investment in physical and human capital (defined as productive public expenditures). • The crises that recently hit the world economy caused a growing presence of the state in the economy. • The issue is dealt with by focusing on the effects of public spending on the economy's growth rate. • Public spending can positively affect the growth rate by raising the economy's productivity. • Productivity is raised by concentrating outlays on investment in physical and human capital. • At higher growth rates the ratio Public Debt/GDP can be stabilized in spite of primary deficits. [ABSTRACT FROM AUTHOR]

The uncertainty of renewable energy necessitates reliable demand response (DR) resources for power system auxiliary regulation. Meanwhile, the widespread deployment of energy-consuming 5G base stations (gNBs) drives internet service providers (ISPs) to seek energy expenses reduction. This paper intelligently addresses these two complementary needs by integrating gNBs into the power system dynamic economic dispatch (DED). To achieve this, a combined DED model that incorporates both the power system and 5G communication network is developed, where numerous distributed gNBs and their backup energy storage systems (BESSs) are integrated as a virtual power plant (VPP) to offer power support and obtain economic incentives. Instead of traditional optimization methods, the combined DED model is solved by a cooperative game-based solution method in a benefit-balanced manner, facilitating the power system and 5G network to achieve a win-win DED solution. This method comprises a two-stage game model and a solution algorithm based on improved random drift particle swarm optimization (RDPSO). The improved RDPSO constructs an uncertainty set in each iteration, facilitating its solution of stochastic optimization involving power system uncertainties. Lastly, a VPP management function is developed within the 5G core network (5GC) to efficiently evaluate the VPP's dispatchable capacity and formulate optimal control policies that can minimize control costs. Simulations based on the IEEE 118-bus system and real device data demonstrate that the proposed method can simultaneously reduce the power system's operating costs and increase the ISP's economic revenue. • Addressing complementary needs by integrating gNBs into power system DED. • Game-based solution for DED balancing supply and demand sides benefits. • Improved RDPSO algorithm for power system DED considering uncertainties. • VPP function efficiently evaluating dispatchable capacity and formulating optimal control policy. • Economic and technical win-win results in simulation based on real data. [ABSTRACT FROM AUTHOR]

This paper proposes a joint optimization framework for the demand response-capacity configuration design of the integrated energy system (IES), in which both the economic cost and closed-loop dynamic regulation performance under source-load fluctuations are considered. The joint optimization consists of two layers. The upper layer optimizes the time of use electricity price to adjust the demand response of electric load; while the lower layer optimizes the capacity configuration, scheduling instructions and incentive-based thermal load demand response under typical scenarios. To fairly evaluate the impact of equipment capacity and demand response on the dynamic control performance of the IES, multi-parameter programming-based predictive control approach is applied to develop an offline design and scheduling-perceptive control system. A closed-loop dynamic scheduler is then developed based on the control system, which can optimize the operating instruction of each equipment considering their dynamic operating practice. Simulation studies on a typical off-grid combined heat and power IES show that the proposed approach can effectively match the demand response to the capacity configuration of the IES, helping the system achieve a dynamically flexible and economic energy supply. • Joint capacity configuration and demand response optimization is proposed for IES. • Dynamic control performance is considered in the optimization. • Design and scheduling-perceptive controller is developed to bridge gap between design and operation. • Dynamic scheduling is proposed to consider IES operating practice in the optimization. • Both economic and dynamic control performance of IES is improved by the proposed method. [ABSTRACT FROM AUTHOR]

The exploration and promotion of new energy constitute a significant initiative for numerous countries in their pursuit of sustainable development and efforts to alleviate climate change. However, the influence of a multitude of uncertain factors on the evolutionary path of new energy is currently poorly studied. By establishing a dynamic evolution model for new energy under polymorphic uncertainty, this paper analyses the complex interaction between new energy, economic growth, and carbon trading over the period 2020–2060, using China as a case study. We employ numerical simulation and sensitivity analysis to investigate the evolutionary trajectories of the new energy system under various parameter value scenarios. We show that the evolutionary trajectories will undergo substantial fluctuations before 2030, subsequently stabilizing from 2030 onwards. The study highlights that an excessively rapid pace of new energy development is detrimental to new energy systems, whereas energy storage system deployment has the opposite effect. There exists a significant risk of the system collapsing as a result of slow new energy development, over-building energy storage facilities, or over-intervening in carbon trading. We also find that overdevelopment of energy storage system may have greater negative impacts on the economic system than underdevelopment. [Display omitted] • Dynamic evolution model of new energy under polymorphic uncertainty is constructed. • New energy production elasticity and energy ratio are innovatively constructed. • Excessive intervention in carbon market can lead to the collapse of new energy system. • Overdeveloped energy storage hinder the synergy of new energy and economic growth. [ABSTRACT FROM AUTHOR]

Renewable Energy Communities (RECs) are being deployed all around the World as a technically feasible solution to decreasing users' dependence on fossil fuels. Several demonstration facilities have shown their potential to provide final consumers with clean energy and all associated environmental benefits. However, the economic evaluation of these systems as a whole set is more complex than evaluating generation technologies individually, which can be considered a barrier, and it may be more complicated to calculate its uncertainty with precision. This paper deals with this challenge and adapts a model for the evaluation of the global Levelized Cost of Electricity (LCOE) of a polygeneration microgrid to the characteristics of a typical REC, allowing the assessment of the distribution of the LCOE depending on the uncertainty of the input parameters. Thanks to its simple analytical formulation, the proposed model, that can be used for any combination of technologies (both renewable and conventional), provides relevant information on uncertainty propagation in a symbolic way that avoids the need to run numerical simulations or make assumptions on the distribution of the random input parameters. A case study has been presented, considering a typical small electrical REC with photovoltaic plants and micro wind turbines. Although the model can be defined to any market, as a representative example, it has been evaluated according to the current Spanish and Italian regulations, which are analyzed in depth with reference to the scientific literature. Results show that uncertainties in parameter estimates give rise to a very large scatter in the LCOE, pointing out a set of quantities whose role is crucial for a reliable estimate, among which electricity purchase and selling prices, yearly power load, and self-consumption / virtually-shared energy rates stand out. [Display omitted] • The global LCOE for a typical REC has been defined. • The uncertainty propagation in the LCOE formulation has been studied. • The proposed model has been applied to an electrical polygeneration REC. • As an example, the Spanish and Italian regulatory frameworks have been considered. • The LCOE of RECs is mainly sensitive to the electricity price and the power load. [ABSTRACT FROM AUTHOR]

Utilizing renewable reservoirs as a primary source of energy in power systems significantly mitigates environmental concerns. This approach, coupled with the integration of various subsystems, yields multiple products, thereby enhancing economic benefits. This paper introduces an innovative multi-generation system based on a gasifier-fed solid oxide fuel cell. In this system, waste heat recovery is achieved through a bespoke transcritical CO 2 cycle, combined with a Kalina cycle and an organic Rankine cycle subsystem. Additionally, a proton membrane electrolyzer is employed to supply a reactor for ammonia production. A comprehensive analysis of this configuration is performed, employing thermodynamic, economic, and environmental assessment methodologies. Moreover, two multi-objective optimization strategies are implemented to determine the system's optimal operating conditions. The results demonstrate that the proposed system generates a net power output of 293.1 kW, provides cooling of 40.8 kW, produces hydrogen at a rate of 2.689 kg/h, and ammonia at a rate of 15.14 kg/h. The environmental and economic impacts of these products are quantified as an exergoenvironmental impact rate of 111.89 Pt/h and a cost rate of 11.77 $/h, resulting in an exergy efficiency of 27.59%. Furthermore, the system's financial feasibility is evident from its payback period of approximately 3.42 years and a net present value of 1.64 M$. The study also identifies that variations in the fuel cell's current density significantly influence the system's exergy performance and environmental footprint. • Design of a novel multi-generation based on the gasifier-SOFC integration. • Application of exergoenvironmental approach. • Producing 2.689 kg/h of hydrogen and 15.14 kg/h ammonia. • Assessing 27.59% exergy efficiency and 111.89 Pt/h exergoenvironmental impact rate. • Achieving a payback period of 3.42 years and 1.64 M$ net profit. [ABSTRACT FROM AUTHOR]

This paper presents the energy management of smart distribution network including integrated system of hydrogen storage and renewable sources. Objective is to assess economic, operation, flexibility, and reliability goals of the distribution system operator. Objective function minimizes costs of operation, reliability, energy losses, and network flexibility. Scheme is constrained by AC optimal power flow equations, network reliability limitation, and integrated system model. Scheme utilizes scenario-based stochastic optimization to model of uncertainties, such as load, parameters of renewable resources, energy prices, and the availability of network equipment. Novelty is modeling and performance evaluation of the proposed integrated system as a type of flexibility resource in the operation of distribution systems proportional to the economic, operation, flexibility, and reliability objectives of the network operator. Numerical results demonstrate energy management capabilities of the discussed integrated energy system, which contribute to enhancing economic and technical conditions of distribution network. The inclusion of hydrogen storage in the renewable integrated energy system has been found to enhance the economic viability, operational efficiency, and reliability of the distribution network by around 46.8%, 41%–53%, and 95%, respectively, when compared to network power flow. Aforementioned integrated system demonstrates 100% flexibility through the utilization of hydrogen storage. • Developing a model for assessing the efficiency and effectiveness of hydrogen storage and bio-waste units farm. • Considering an integrated energy system to evaluate the economic and technical objectives of the DSO. • The utilization of an integrated hydrogen storage and RESs system as a means to enhance the network flexibility. • Simultaneous modeling of economic, operation, reliability, and flexibility objectives of the DSO. [ABSTRACT FROM AUTHOR]

Cross-regional energy sharing is a promising way to alleviate the imbalance in energy distribution. However, there is a risk that the uncertain fluctuations of intermittent renewable energy sources (RES) could be transmitted across regional transmissions, resulting in large-scale system economic inefficiency and security concerns. To this end, a unified spatial-temporal cooperative framework for the integrated energy system, which considers the coordination between intra-regional multi-energy coupling and cross-regional multi-agent-system energy sharing, is proposed in this paper. First, in the temporal dimension, a multi-time-scale stochastic decision-making model is established based on stochastic model predictive control (SMPC). Thus, slow-response units are arranged to gain economic goals in day-ahead stage and the closed-loop rolling adjustment is implemented on fast-response units to achieve accuracy supply-demand balance in real-time stage. Second, in the spatial dimension, a fully distributed dispatch approach is adopted to address the information sharing barriers, which only requires neighboring information and considers consistency of boundary control variables on border lines. Finally, to improve the computational efficiency, the established complex time sequential model is decomposed into a set of time-discrete parallel sub-problems, using the optimal condition decomposition (OCD) technique. Simulation results on a cross-regional test system demonstrate that proposed framework can increase the wind power consumption capacity and reduce operation cost through cross-regional cooperation, while the parallel decomposition can save 67.25 % of the computation time relative to the serial computation. • A novel unified spatial-temporal cooperative dispatching framework is proposed for the cross-regional IES. • A multi-time-scale rolling stochastic decision-making model is established to handle RES uncertainties. • The ADMM is implemented to achieve cross-regional rapid interactions iteration with only neighboring information. • The OCD is applied to decompose the complex spatial-temporal problem. [ABSTRACT FROM AUTHOR]

Sustainability and Industry 4.0 have been emerging topics in recent years, in theory as well as in practice. Related technological topics like 5G technology, blockchain, artificial intelligence and digital twins among others, are gaining importance. In addition, these themes are becoming increasingly merged with a discussion on I4.0 from societal and organizational perspectives. As a background to this paper, we discuss recent research in this area as covered in this Special Issue. We identify accepted submissions covering four conceptual themes and three methodological themes in the SI. Finally, we outline opportunities for future research: these include human interaction with I4.0 technology-enabled workspaces as well as societal aspects of I4.0 in relation to sustainability efforts. Last, but not least, interaction of I4.0 with other disciplines, such as sustainable manufacturing, reliability, maintenance and disaster management are proposed. • Sustainability and Industry 4.0 (I4.0) are emerging topics from societal and organizational perspectives. • Related technological topics like blockchain, artificial intelligence, digital twins among others, are gaining importance. • This SI outlines opportunities for: human interaction with technology-enabled workspaces and societal aspects of I4.0. • Interaction of I4.0 with other disciplines, such as sustainable manufacturing, disaster management, are proposed. [ABSTRACT FROM AUTHOR]

• We reconstructed a complex wetland with nature-based solutions. • This eco-economic system was driven by renewable emergy flows and high exergy. • The mulberry-related industry provided good economic benefits. • The stable equilibria of the eco-economic coupling mode represented a state of synergistic symbiosis. Nature-based solutions (NBS) are effective approaches for solving ecological and economic problems related to wetland reconstruction, while emergy and exergy are well suited for evaluating reconstructed eco-economic systems. In this paper, we executed a wetland reconstruction project involving a mulberry-fir woodland and dike-pond complex (MFWDPC) with NBS in the water-level fluctuation zone of the Three Gorges Reservoir (TGR), China. We evaluated the coupling mode of this eco-economic system by using emergy and exergy theories. After wetland reconstruction, the emergy density that entered the MFWDPC was 1.48E + 18 sej yr−1 hm−2, in which the naturally renewable flow accounted for 96.31 % of the emergy inflows, and the emergy sustainability index (ESI) for the eco-economic system was 708.88. The exergy and specific exergy were 2.41E + 14 j yr−1 and 2.08E + 08 j yr−1 kg−1, respectively, and the emergy/exergy ratio was 1.55E + 03 for the MFWDPC. In addition, mulberry trees with strong flooding tolerance provided good economic benefits due to the use of these plants in reconstructed eco-economic systems. Our study of NBS for wetland reconstruction helps in understanding the synergistic symbiosis between ecosystems and economic systems, thereby promoting the reconstruction of degraded wetlands in China and other regions. [ABSTRACT FROM AUTHOR]

• A composite index named GLCDES is developed to dynamically assess the level of Green Low-carbon Circular Development. • The combined-assignment VIKOR model based on game theory is developed to thoroughly estimate the construction level of GLCDES. • Southwest China's Green Low-carbon Circular Development Economic System construction level is examined from 2012 to 2021, showing how the long-side principle governs each region's value. • Selecting and strengthening the long side in combination with its reality is an effective way to improve the construction level of GLCDES. Emission peak and carbon neutrality are critical components of China's overall ecological civilization, and the establishment of a sound Green Low-carbon Circular Development Economic System (GLCDES) is a fundamental countermeasure for solving China's resource, environmental, and ecological problems. This paper used the new development concept as entry points to establish a comprehensive evaluation index system encompassing six subsystems: development dynamics, production and living systems, green benefits, low-carbon benefits, circularity benefits and socio-economic benefits. The combined-assignment VIKOR model, based on game theory, was used to thoroughly estimate the construction level of GLCDES by combining panel data from four southwestern provinces, including Chongqing, Sichuan, Guizhou and Yunnan, from 2012 to 2021. The study discovered that: the construction level of GLCDES in all four southwestern provinces increased over the past ten years, but the increase was not significant, the four development effectiveness indices, including green, low-carbon, circularity and socio-economic, were significantly improved, the development dynamics index was not significantly enhanced, and the index of production and living systems declined in fluctuation from year to year. The construction level of GLCDES between regions was very uneven and can be divided into two echelons, with the gap between Chongqing and Guizhou being particularly obvious. Regions can be categorized into three major categories based on the number of dominant subsystems. According to the above results, the total value of the dynamic comprehensive evaluation of each region is governed by the principle of the long side, which is an effective way to improve the construction level of GLCDES by selecting and strengthening the long side in combination with its reality. [ABSTRACT FROM AUTHOR]

In this paper, the development of the blue economy is considered from the perspectives of integrity, relevance, and operability. First, to further expand blue living space and achieve blue growth, development thinking must shift from supporting the development of a single agent to supporting the coordinated development of multiple agents. Thus, we set a list of 116 blue products. Second, we construct a conceptual model of the national blue economic system space. Taking China as an example, the product data, firm data and province data in China's blue economic system are collected and processed based on the needs of the study, and the basic dataset for constructing China's blue economic system space is obtained. Third, based on the blue product space, the blue firm network and the blue province network of China, as well as the mapping relationship that existed among them in 2010, China's blue economic system space in 2010 is constructed. Finally, evolutionary maps of China's blue economic system space from 1985 to 2018 are drawn to identify the spatial structure and characteristics of China's blue economic development. We use application examples to verify that the national blue economic system space can play an important role in assisting countries in conducting systematic multicriteria assessments of blue growth strategies, and the use of such a complex network makes countries more likely to formulate evidence-based policies without being influenced by vested sectoral interests. [ABSTRACT FROM AUTHOR]

The current coronavirus pandemic has produced severe consequences on economic and health systems all over the world, with the governments being challenged in searching for containment solutions balancing virus diffusion and limitations to social and work activities. In this paper, we propose a framework for the real-time optimization of restrictions in epidemics, based on the use of a time-varying SIRD model. Despite their simplicity, this class of models is able to capture the essential features of the epidemic spread, with the inherent parameter variation allowing accurate adaptation to real data. An optimization problem is formulated, properly balancing health and economic costs, and is solved parametrically by following a receding-horizon approach, resulting in an optimal sequence of social contact restrictions, which are assumed to be actuated via governmental containment measures. Numerical simulations based on the real data of the Italian COVID-19 emergency highlight the potential of the proposed approach and can be possibly helpful for the decision makers in present and future pandemics. [ABSTRACT FROM AUTHOR]