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Highlights • We evaluate paper-level impact metrics on field bias, time bias, and ranking performance. • We evaluate mean-based, percentile-based, co-citation-based, and post hoc rescaling approaches to normalise citation scores. • Percentile-based citation scores are less field and time biased than mean-normalised citation counts. • No significant difference in ranking performance exists between percentile- and mean-normalised citation scores. • Citation counts are always less time biased but always more field biased than PageRank. Abstract We evaluate article-level metrics along two dimensions. Firstly, we analyse metrics' ranking bias in terms of fields and time. Secondly, we evaluate their performance based on test data that consists of (1) papers that have won high-impact awards and (2) papers that have won prizes for outstanding quality. We consider different citation impact indicators and indirect ranking algorithms in combination with various normalisation approaches (mean-based, percentile-based, co-citation-based, and post hoc rescaling). We execute all experiments on two publication databases which use different field categorisation schemes (author-chosen concept categories and categories based on papers' semantic information). In terms of bias, we find that citation counts are always less time biased but always more field biased compared to PageRank. Furthermore, rescaling paper scores by a constant number of similarly aged papers reduces time bias more effectively compared to normalising by calendar years. We also find that percentile citation scores are less field and time biased than mean-normalised citation counts. In terms of performance, we find that time-normalised metrics identify high-impact papers better shortly after their publication compared to their non-normalised variants. However, after 7 to 10 years, the non-normalised metrics perform better. A similar trend exists for the set of high-quality papers where these performance cross-over points occur after 5 to 10 years. Lastly, we also find that personalising PageRank with papers' citation counts reduces time bias but increases field bias. Similarly, using papers' associated journal impact factors to personalise PageRank increases its field bias. In terms of performance, PageRank should always be personalised with papers' citation counts and time-rescaled for citation windows smaller than 7 to 10 years. [ABSTRACT FROM AUTHOR]

Emission permitting (EP) has been widely implemented to regulate point-source wastewater emissions. However, the ways in which local industries respond to an EP intervention and to what extent an EP policy could reduce emissions is subject to great uncertainty and should be investigated. This study develops an agent-based model to examine each individual firm's changing behaviours and the aggregated environmental and economic performance of the local economy. The Baoding Municipality, located upstream of the Baiyangdian Wetland, a core conservation area in the Xiong' An New District, was investigated as a case study. The simulation results showed that the regional industrial economy first fell and then rose. The chemical oxygen demand (COD) emissions of the municipal industrial total showed a downward trend from 2015 to 2025, with an average annual reduction of 1.2% under the current EP policy. The textile and papermaking industries had comparatively large potential for emission reduction, accounting for 43.7% and 22.7% of the total industrial emissions, respectively. The simulation results also showed that over 61.9% of firms changed their approach from take-no-action to proactive pollution control up to 2025. Further, the results verify that total emissions would be expected to decrease significantly with tightening permit limits, while the marginal effects of EP regulation would be expected to decline gradually. As a quantification of EP's effects on both environmental and economic aspects, this study provides valuable insights for the advancement of EP policies through national reform. [ABSTRACT FROM AUTHOR]

This paper aims to analyze the individual's socioeconomic and demographic determinants of financial inclusion and its barriers among for South Asian countries in the lens of existing financial inclusion policy, using World Bank Global Findex database for the period 2011, 2014, 2017, and 2021. We use the Probit regression model to explore the main objective of this study. In addition, we also analyze the trend, pattern, and barriers of financial inclusion for the period 2011- 2021 to identify differences among south Asian countries. Our results reveal that Sri Lanka is the best performer in the inclusivity of financial products as well as removing barriers to financial inclusion in all four periods. While Pakistan and Afghanistan are the least financially included countries, also they failed to remove barriers to financial inclusion. Moreover, our empirical results suggest that individuals who are male, older, wealthier, and more educated are more likely to access financial services, with income and education exerting a higher influence. Further, age shows a non-linear (inverted U-shaped) relationship with financial inclusion indicators. Additionally, we found that individuals having a formal account are the most important indicators of financial inclusion. And the reasons for financial exclusion (i.e., not having an account) are mainly voluntary among South Asian individuals. In fact, it is found that policies like Pradhan Mantri Jan Dhan Yojana for India and National Financial Inclusion Strategy across all South Asian countries play a significant role in accelerating financial inclusion and helping in removing its barriers with different magnitudes. Therefore, our finding stresses the importance of heterogeneous integrated policy measures for the targeted groups of the population, particularly the most vulnerable group among South Asian countries. [ABSTRACT FROM AUTHOR]

In continuation of the previous part, this paper comprehensively focuses on energy, exergy, exergo-economic, and environmental (4E) analyses of two prioritized clean hydrogen (H 2) supply chains pathways. The first scenario relies on domestic demand , involves blue H 2 production and storage using steam methane reforming coupled with carbon capture and storage through H 2 conversion to hydrogen carriers in liquid form (ammonia and methanol) aligned with the existing infrastructure. The second scenario involves green H 2 production from photovoltaic panels as the power source follows by storing H 2 in liquid form which is more suitable to export. The findings indicate that the blue H 2 system outperforms the green H 2 system in terms of energy and exergy, boasting 41.38% and 22.15% compared to 12.16% and 12.75%, respectively. However, when examining economic parameters, the blue H 2 system shows a total cost rate of 1976 US$/h, a net profit of $747.2 million (mn) at the end of its lifetime, and a payback period of approximately nine years. Conversely, the green H 2 system demonstrates economic indicators of a 125.2 $/h as a total cost rate, $1.26 billion (bn) net profit, and an 8.17 year payback period. Moreover, the production cost rates for blue and green H 2 are 1.32 $/kg and 2.55 $/kg, respectively; while the CO 2 emissions rate in the blue H 2 system is 10.71 kg/s. The green H 2 system prevents 19.84 kg/s of CO 2 emissions resulted a significant environmental cost reduction of 2.28 $/kg H 2. This analysis facilitates a thorough comparison of these two clean H 2 production and storage systems, offering insights for policymakers across performance, economic, and environmental dimensions. • A comprehensive 4E analysis was applied for the selected H 2 value chain scenarios. • Blue H 2 scenario is superior economically while environmentally the green one takes precedence. • Carbon credit makes the green H 2 scenario emerges frontrunner utilizing PV solar technology. • Methanol was recognized as the most cost-effective H 2 carrier for storage in Iran. [ABSTRACT FROM AUTHOR]

Considering the different dynamics of the COVID-triggered real shocks and macroeconomic measures during the first two waves of the pandemic, using official quarterly data for 2020 the paper studies how economic support measures have impacted the economic performance of sectors in EU countries. The paper shows that: (1) for all analysed sectoral groups, the economic support measures effects were much smaller than the corresponding non-pharmaceutical interventions impacts; (2) sectoral groups did not receive economic support in proportion to the corresponding nonpharmaceutical interventions; (3) sectoral groups with greater non-pharmaceutical interventions did not receive even systematically bigger effects of the economic support measures; and (4) the net overall effects of the COVID-triggered measures on sectoral value added vary considerably between sectors. By evaluating the potential opportunity costs of the 'flat' economic support activity, the study's results also help to identify measures that could eventually be implemented once the pandemic has ended to mitigate the potential opportunity costs of the sector non-specific implementation of economic support measures, which will become apparent in the recovery period following the pandemic. [ABSTRACT FROM AUTHOR]

• The paper documents developments in Indian economic performance and the role of structural change in this performance. • Manufacturing is dominated by informal labor, which has a deleterious effect on overall manufacturing performance. • Productivity growth is driven by within sector growth, with positive contributions of formal and informal activities to this. • Labor reallocation to the informal segment of the manufacturing sector is growth reducing in the Indian manufacturing sector. • A shift to more productive informal activities across sub-sectors exists, but this doesn't offset the negative within effect. This paper examines the role of structural change in India's performance, and in particular the role of the informal segment in the stagnant growth of the Indian manufacturing sector. Using counterfactual analysis – including a modified shift-share approach – the paper considers to what extent the informal sector can help explain the poor performance of Indian manufacturing over the period 1980-2011. The results suggest that the informal segment of manufacturing has been a drag on overall manufacturing productivity growth, with the informal manufacturing segment unable to provide a productivity bonus when absorbing workers from the agricultural sector. Results from the shift-share analysis indicate that labor reallocation to the informal segment of the manufacturing sector is growth-reducing, with structural change being the main source of this reduction, an effect which in turn is driven by movements of employment within manufacturing sub-sectors. [ABSTRACT FROM AUTHOR]

Over the last two decades global economy and financial markets have seen many crises with an increasing frequency. Many of them were either unexpected or their effects were unpredictable. Therefore, a lot of conventional and popular investment opportunities have shortened or became less attractive for either private or institutional investors leaving a room for new instruments and products to gain popularity. Unlike new volatile and uncertain markets like cryptocurrency, there are potentially more stable processes such as M&A activity, which can be predicted to earn abnormal returns. In this paper, we study various financial and non-financial indicators of acquired and non-acquired companies to provide a set of variables that can describe a company from different perspectives. Next, M&A prediction model is designed. Then, techniques are discovered to increase its explanatory and predictive power, and flexibility making it applicable for different economic environments without being harder to implement it by a potential user. In the end, its efficiency is measured on a real data to compare it with a result of methods found in earlier papers. [ABSTRACT FROM AUTHOR]

• ADA learns preferences from market data and predicts price of newly generated building design. • Integrate ADA with 3D building simulation to study price gain and value at risk. • Changes to Visual Capital identify the exposure and financial sensitivity to land-use change. • Blueprint for integrating economic evaluation within site-selection, design optimization. Uncertainty of spillover effects – including property devaluation - from proposed land-use change elicits opposition to local development. This hinders cities' ability to implement land-use policy aimed at housing affordability and environmental sustainability. The uncertainty arises from the limitation of current methods to quantify both the physical exposure and financial sensitivity to changes in urban environmental characteristics and amenities. Hence, this paper introduces an integrated approach to quantify the direct and indirect effects of a proposed urban development, using parametric modeling, building performance simulation and automated design appraisal (ADA). ADA, a novel aspect of this study, predicts the market price of a generated building design concept within a local urban context. To demonstrate its practical utility, this paper examines property value at risk due to nearby development associated with changes to visual landscape quality. The results shed light on the importance and spatial variability of the price-amenity gradient, identifying properties that are financially at risk to the visual impacts of local development. Beyond building-specific risk modeling for the development planning stage, this work serves as a blueprint for architectural design optimization procedures, in which economic performance is evaluated based on learned preferences derived from financial market data. [ABSTRACT FROM AUTHOR]

For policymakers who intend to make decisions on the policy to stimulate economic growth and enhance job opportunities, business demography statistics serve as important data sources for promoting entrepreneurship, which is a key for the policy. Business demography, which usually consists of annual demographic information such as the number of births, deaths, and survivals of businesses, can be produced from a business register in many countries. In Japan, however, it is difficult to produce business demography directly from the Japanese business resister called Establishment Frame Database because the major data source for the database is the Economic Census, conducted twice every five years. Alternatively, business demography can be estimated using the data of the Economic Census. However, it has been pointed out that short-lived births of businesses are hardly grasped by the estimation using the data of statistical surveys such as the Economic Census at intervals of more than one year. This paper introduces a stochastic model to cope with the above issue and proposes a calculation formula to estimate the number of short-lived births of businesses as well as other demographic indicators of business demography. Then the paper is followed by a numerical example, which proves to overcome the shortcomings of previous methodologies. [ABSTRACT FROM AUTHOR]

Statistical modeling and forecasting are very important for decision-making in any field of life. This paper has two major objectives, namely, statistical modeling and forecasting of real phenomena. For covering the first aim (i.e., statistical modeling), we introduce a new probabilistic model. The new model is introduced by mixing the Dagum distribution with the weighted T X family approach. The proposed model is called the weighted T X Dagum distribution and possesses heavy-tailed characteristics. The new model is illustrated by analyzing real-life data related to Bitcoin prices. To cover the second aim (i.e., forecasting), we take into account six macroeconomic and financial indicators to investigate their impact on Bitcoin prices such as the Adaptive least absolute shrinkage and selection operator (Alasso), elastic net, and minimax concave penalty. After analyzing the data, it is found that Alasso and MCP have retained all the included predictors, except import, while Enet holds all the predictors. The root means square error and mean absolute error associated with MCP are lower than Alasso and Enet, which reveals that MCP fits the data very well as compared to rival methods. [ABSTRACT FROM AUTHOR]

In this "Data, Tools and Replication" paper we adapt the complexity algorithm by Hidalgo and Hausmann (2009) to the financial sector and compute a measure of financial development that is intrinsically linked to its diversity dimension. We then test the impact of financial development on financial stability, economic resilience and growth, focusing on Italy. By employing an index that levers on the diversity dimension of financial development, our findings reveal that higher degrees of financial development translate into a lower financial fragility and a more resilient real economy. Indeed, territories denoted by more diverse and developed financial systems show an increased firm natality rate, a reduced firm mortality rate, and a lower probability of experiencing employment and GDP drops. Finally, higher financial development also spurs economic growth. Results are confirmed at micro-level, and higher levels of financial development and diversity decrease the probability of firms' exit from the market and enhance their investment activity. Our findings unveil the importance of considering the diversity dimension of financial development when investigating its role on economic performance. • Drawing from network theory we calculate a measure of development of financial systems that levers on its diversity dimension. • Using Italian data we investigate whether financial development and diversity improve financial stability, the resilience of the real economy, and economic growth. • We find that more developed and diverse financial systems are more stable. • Benefits extend to the real economy, that shows greater resilience and growth. • Results emerge both in a micro and more aggregated setting. [ABSTRACT FROM AUTHOR]

Understanding the relationship between energy price dynamics and competitiveness is key to the debate on the transition to environmental and economic sustainability. That is particularly the case in emerging economies, where sectors and firms are less mature and more fragile to external shocks. Using a panel of firms in 12 sectors and 11 high and middle-income countries over the years 2002–2013, this paper provides evidence that growing energy prices do not harm and in some cases can even strengthen economic performance. The results suggest that the size and direction of the impact of growing energy prices depends on firms' energy intensity, among other characteristics. This should be taken into account in empirical studies focusing solely on average effects. • A widespread view is that growing energy prices determine a reduction of profits and competitiveness of firms. • This study analyzes the impact of energy prices on performance indicators of firms in developing and emerging countries. • We find that rise in energy prices can trigger innovation, which may compensate the static losses of rising input costs. • The negative impact of energy price changes on performance is stronger for firms with a higher level of energy intensity. [ABSTRACT FROM AUTHOR]

Economic inequality, in terms of income or wealth, is one of the most complex and perplexing challenges of our current capitalist economic system. While the dynamic relationship between income inequality and economic growth has been extensively investigated (since the beginning of the last century), the dynamic link between wealth inequality (or wealth concentration) and economic growth has been largely ignored in the literature, taking more attention in recent years. This paper asserts that the accumulation of non-productive assets/luxury goods is an important determinant of wealth inequality, as well as a determinant of the relationship between wealth inequality and economic growth. In this paper our aim is to show that an increase in wealth inequality is associated with a slowdown in economic growth. In particular, the paper shows that there is a negative relationship between wealth inequality and economic growth in France because poor households own a relatively large fraction of non-productive luxury goods. Nevertheless, this is not the case in the US, which is what explains that such negative relationship is not observed there. We conclude that a redistribution of wealth (from the rich to the poor) is important for attaining a sustained economic growth performance. • This paper contributes to the analysis of economic growth and wealth inequality in France and the United States. • We propose a macroeconomic model that includes both productive and non-productive assets. • Economic growth slows as the aggregate stock of non-productive assets increases as wealth inequality increases. • Redistribution will enrich the poorest households and therefore decrease their propensity to own unproductive assets. • Wealth inequality and economic growth may have a non-linear relationship. [ABSTRACT FROM AUTHOR]

The medium- and long-term operation strategy of the hybrid power system needs to adapt to the continuous changes in the market. This paper presented a medium-term (monthly) operational simulation model and nested it into a long-term (yearly) optimization model. It was solved by the genetic algorithm (GA) to maximize yearly revenue. The planned delivered-electricity to power process constraints were present to improve the optimization efficiency. Using penalty price factor (α) and threshold of exempted punishment for the supply shortage δ to characterize the punishment for supply shortage risk. This framework was applied for a new hybrid power system so-called LCHES-WP (large-scale cascade hydropower energy storage system, wind power, PV power). It can participate in the electricity trading market with a bundled output pattern. The real cascade hydropower stations in Qinghai Province, China, are selected as the case study. In the baseline scenario, the current time of use (TOU) electricity price and punishment level (α = 0.05, δ = 5%) were considered. It reveals that (1) the revenue (baseline scenario, 6.15351 billion CNY) in the bundled output pattern is higher by 45.6% than in the separate output pattern. (2) the economic and technical indicators are sensitive to α , but not to δ. α increases make the punishment stricter, supply shortage risk was significantly reduced due to the probability of the shortage event, the maximum monthly supply shortage ratio and punishment electricity are reduced by up to 22.5 percentage points, 26.7 percentage points, and 1880.1 GWh, respectively. The yearly revenue is reduced by up to 4.7%. the reason is the yearly curtailment is increased and the penalty amount does not change dramatically with the changes in punishment. (3) The α = 0.6 and α = 1.8 can be regarded as demarcation point and upper boundary point for the case of hybrid power system, respectively. It is proposed that when planning a capacity scheme, stakeholders should consider that the demarcation point is higher than the current α. In general, this paper provides a reference for the medium- and long-term operation optimization of the LCHES-WP hybrid power system in the future. • Electricity settlement rule in medium and long term electricity trading is considered. • The monthly operational simulation model was nested in the yearly optimization model. • Bundled output pattern can rise by 45.6% of revenue than the separate output pattern. • The supply shortage risk is significantly reduced by increasing the punishment level. • The penalty amount does not change obviously with the changes in the punishment level. [ABSTRACT FROM AUTHOR]

To fully extract the potential of offshore wind power, the wind farms are built further from mainland and the capacity is growing larger. Low Frequency AC (LFAC) transmission System is a promising solution to the integration of large offshore wind farms in long distance. This paper conducts a comparative economic analysis of LFAC. Compared to 50 Hz High Voltage AC (HVAC) transmission, LFAC can reduce charging current in AC cables to enhance the transmission ability in long distance. Compared to High Voltage DC (HVDC) transmission, LFAC eliminates the large expense of offshore converter station. In addition, the topologies of AC/AC converter in LFAC also influences the economic performance significantly. By designing typical AC/AC converters based on modular multilevel technique in a unified way, this paper performs a full-scale economic comparison in different voltage level, active power and reactive power cases. Furthermore, the economic comparison based on capital cost of HVAC, HVDC and LFAC is comprehensively studied considering different transmission capacity and the AC/AC converter topologies. The economic range of HVAC, LFAC and HVDC application is obtained, and results show that LFAC is suitable for medium distance circumstances (around 80–200 km). The economic range of LFAC expands when power rating increases. • Economic range of HVAC, HVDC and LFAC is analyzed according to their capital cost. • Converter topologies and transmission capacity is considered in the cost of LFAC. • Comprehensive comparison of four AC/AC converter topologies for LFAC is conducted. • A unified method is introduced to design four AC/AC converter topologies. [ABSTRACT FROM AUTHOR]

Systems involving more than one energy source, namely hybrid systems, can be arranged in different configurations and layouts depending on the available or used energy source, application and scale of the system. Despite an increasing awareness and penetration of renewable energy sources at different scales in many countries over the world, the adoption of hybrid and novel renewable systems is relatively scarce, especially at a small scale. This scenario is also presented from the point of view of the research of such systems. Therefore, the scope of the paper is to improve the knowledge regarding small-scale hybrid renewable energy systems performance and operation by investigating a novel trigeneration system based on a biomass fired steam cycle, wind turbine, photovoltaic panels and adsorption chiller. In the proposed system, the thermal energy recovered by the steam cycle is used to match the user thermal demand by means of a storage tank supplying directly heat to the user during winter, and driving the adsorption unit during summer. The electrical energy produced by the steam turbine, photovoltaic field and wind turbine is managed by a control system that integrates a bidirectional connection with the electric grid. The grid allows one to virtually store the electrical energy produced in excess and to recover it in part when needed by the user. In order to assess the system performance, a farm and residential buildings are considered as case study. The proposed system is modelled and dynamically simulated by means of Transient System Simulation software. In the paper, the dynamic operation of the system and the energy and economic performance of the system is assessed. The operation of the system is presented in a daily and monthly basis, while the global performance parameters of the system are reported considering a one-year period. The primary energy saving of the system is above 70% when a reference system based on natural gas, electric chiller and grid is considered. For this scenario, the Simple Pay Back of about 10 years is achieved. The results of the analysis show that the system is feasible from the technical and energy point of view, whereas its economic profitability is significantly affected by its cost and the reference system that is adopted by the user. • Modeling and dynamic simulation was performed with TRNSYS software. • Energy and economic performance of the system was assessed. • Different reference systems are considered for heating. • Primary Energy Saving ratio was between 57.4 and 73.3%. • The minimum Simple Pay Back is about 10 years. [ABSTRACT FROM AUTHOR]

Distributed energy systems are becoming increasingly popular worldwide. The 100% renewable energy system can be an important energy supply alternative to reduce the carbon emissions and address energy shortage, especially for remote areas. However, the performance of 100% renewable energy system (RES) at community level needs further detailed analysis, which is affected by the climates, availability of renewable energy and local energy markets. This paper proposes a design optimization framework of 100% renewable energy systems for low-density communities and investigates the system performance. To investigate the integration and performance of 100% RES, thirty typical cities located in different regions of China were chosen considering different climates, geographical features, and renewable energy distributions. By taking the economic performance as the optimization objective, the optimal design for the 100% RES is obtained, and the energy and economic performance is analyzed. Results show that, under the current energy market conditions, the 100% RES is feasible for low-density communities in most regions of China. The payback periods of the systems in most cities are less than six years. When the cost of PV is reduced by half considering future technological developments, the payback period of a 100% RES can be reduced by 30%–60%. This paper would provide design suggestions and application recommendations in regard to promoting 100% RES in China. • Application of 100% renewable energy system at community level is studied. • 30 zones are selected considering climates, renewable energy sources and markets. • Solar energy and biomass can contribute to over 90% of the total energy supply. • Investment of 100% RES can be paid back in 6 years for most regions. • Payback periods can be reduced by 30%–60% when PV technology is further developed. [ABSTRACT FROM AUTHOR]

• Our paper highlights how and why state capitalism impacts energy democracy and renewable energy deployment. • The timing of the paper is critical because state capitalism is attracting more power and attention around the world. • We conclude that the values of state capitalism contradict with the necessary conditions of creating energy democracy. • We expect a deterioration or slowness in energy democracy diffusion and renewal energy deployment. The current paper highlights the interconnection between the state capitalist system and the concept of energy democracy. Both terms have attracted the attention of scholars because of significant economic, environmental, and political events. Our paper reviews the literature to show that state capitalism and energy democracy cannot exist together because the values of state capitalism contradict the necessary conditions to generate energy democracy. Moreover, globally, Western capitalist countries are failing in terms of their economies and political performance and yet, at the same time, economic performance is shifting toward state capitalism with less importance given to democratic actions. Therefore, we expect a deterioration or slowness in energy democracy diffusion and renewable energy deployment. For state capitalism to generate geographical representation, it needs to use regulations and spread renewable energy technology. [ABSTRACT FROM AUTHOR]

A long tradition in economic theory has seen the real exchange rate (RER) as a key determinant of trade performance. Several empirical studies, however, have found low estimates of macro trade elasticities and, as a result, questioned this argument. In this paper, we show that to understand and estimate the effect of RER on trade performance, it is crucial to explore the existence of heterogeneous responses of individual products to RER movements. Using trade data from Argentina, we employ the Mean Group method to estimate macro trade elasticities by individual products disaggregated at four digits of the SITC; rev.2. We find a wide range of heterogeneous responses of exports and imports to RER movements at the individual product level. We find that the estimated RER-elasticities in differentiated products and labor-intensive manufacturing goods are substantially larger than those of primary and homogeneous products. Based on the estimated elasticities at the product level, we obtain a "low" aggregate RER-elasticity of exports when we weigh them by the country's trade basket. We show that this result is a consequence of Argentina's economic structure, a country whose exports are heavily specialized in primary and homogeneous products. Our results are important because they help build a bridge between two conflicting views in the RER-economic development literature. Even in countries with "low" aggregate macro RER-elasticities, the real exchange rate may be an important variable for economic performance. An undervalued RER may facilitate economic growth by stimulating investment in activities producing more complex/differentiated goods in countries that specialize in the production of primary and homogeneous goods and, as a result, have "low" trade elasticities. This seems to be the case of several countries in Latin America, including Argentina. • We find a wide range of heterogeneous responses of exports and imports to RER movements at the individual product level. • The estimated RER-elasticities in differentiated products and labor-intensive manufacturing goods are substantially larger than those of primary and homogeneous products. • Based on the estimated elasticities at the product level, we obtain a "low" aggregate RER-elasticity of exports when we weigh them by the country's trade basket. • We show that this result is a consequence of Argentina's economic structure, a country whose exports are heavily specialized in primary and homogeneous products. • Our results are important because they help build a bridge between two conflicting views in the RER-economic development literature. Even in countries with "low" aggregate macro RER-elasticities, the real exchange rate may be an important variable for economic performance. [ABSTRACT FROM AUTHOR]

• The present paper develops an innovative methodology to analyze the distributional changes in the RPC in the last decades using CHNS and CHIP data. • The paper documents how the distributional changes hollowed out the middle of the Chinese household income distribution and increased the polarization. • We define the polarization as the combination of divergence from global and convergence on mean local incomes. The method used is the Relative distribution one (Handcock and Morris, 1998 1999). • Our results seem to be in opposition to the widely shared received idea that in the last decades the Chinese middle class consolidated its status, increasing its weight in the total population and capturing a rising share of the national income. • Even in a fast-growing context, socioeconomic polarization is a serious problem - especially so in a country like China, with its unique historical, cultural and ideological background. Since the 1990s, and until the beginning of the second decade of the XXIth century, record economic growth in the PRC has been inclusive (as virtually all social groups experienced sizeable well-being improvements in an absolute terms). Yet, it has been accompanied by an increase in both income inequalities and polarization. To jointly explore these phenomena, we apply the Relative Distribution tool on different datasets. This technique allows to statistically isolate and analyze separately the pure growth and the pure distributional components, or effects, of the overall societal income evolution process. Our main result shows a hollowing out of the mid-range deciles - i..e, the (national) middle class , understood by definition as the central ones of a country's income distribution function - with a corresponding fattening of the highest and lowest ones. Trends in " pure distribution " (i.e., the shape effect) show the emergence of a typical polarization profile. This key finding does not contradict the stylized fact that such a (negative) "pure distributional" trend has been " countered" by impressive GDP growth. The latter has led an ever-increasing and now ample share of China's population to join the ranks of the global middle class - a popular declination of the middle class concept with its own merits, yet totally distinct to the national one mentioned above. 1 1 On the growing number of Chinese households joining the global middle class see, among others, Li He 2006 , Cheng Li (ed.) 2010, Zhang Yuan, Guanghua Wan, and NinyKhor 2011 , Tsang 2014 , Rocca 2017 , Sicular , Gustafsson and Xiuna Yang 2017. We need to take both growth effects and shape effects over time into consideration for a proper political economic assessment of Chinese economic performance. As growth slows, unless countervailing policies are undertaken, polarization might reveal itself more sharply, increasing the risk of social conflicts. To avoid such an unwelcome outcome, focused anti-polarization actions are warranted, along with policies aiming at achieving a more egalitarian growth pattern. [ABSTRACT FROM AUTHOR]

• Innovative combination of offshore solar PV and wind farm. • Increase of capacity factor or grid connecting cable. • Profitability depends on marginal power delivered to the grid and PV costs. In this paper, a techno-economic analysis is performed to assess the feasibility of adding an offshore floating solar farm to an existing Dutch offshore wind farm in the North Sea, under the constraint of a certain fixed cable capacity. The specific capacity of the cable that connects the offshore park to the onshore grid is not fully used due to the limited capacity factor of the wind farm. The principle of cable pooling allows to add floating solar capacity. Using weather data it is found that adding solar capacity leads to forced curtailment due to the cable capacity, but this is quite limited as result of the anti-correlation of the solar and wind resource. For the economic analysis, different scenarios regarding subsidy measures are considered for the calculation of net present value and levelized cost of electricity. Also the optimum additional PV capacity for each scenario is computed. The results show that with higher cost per Wp, optimum PV capacity decreases, but more favourable subsidies lead to higher optimized PV capacities. As the aim of the paper is not limited to a case study a methodology is developed for generalization of the techno-economic analysis of a hybrid solar/wind park. In this generalization, the initial investment, system degradation, cable capacity, number of hours when each system is active, and energy price, are implemented to compute the optimum PV capacity regarding the net present value as an indicator for economic analysis of the project. [ABSTRACT FROM AUTHOR]

• GHG emissions of Arctic LNG transportation are estimated. • Emissions related environmental costs are accounted. • Unit cost of LNG transportation via Northern Sea Route is estimated. • LNG may be the most efficient fuel in total cost saving and emission reduction. • Policy suggestions for energy use on Arctic routes have been recommended. The Northern Sea Route (NSR) might be a promising route for LNG transportation from the LNG plants in Russia's Yamal Peninsula to Northeast Asia countries with significant savings in sailing distance and time compared to traditional Suez Canal Route (SCR). However, surging transportation activities along NSR might harm the environment with the greenhouse gas (GHG) and atmospheric pollutant emissions. Little research has been done to investigate the environmental and techno-economic potential of transporting LNG via NSR. Furthermore, this paper analyses the relevance of an Arc4 ice-class vessel, not currently used for this route. This paper targets to address the question that how much GHGs and atmospheric pollutants does a carrier emit and how is the feasibility of different fuel types for the summer and winter transportation considering environmental impacts. An environmental and techno-economic model quantifying the emissions and total costs have been developed and 3 scenarios of using different fuel types (HFO, MDO, LNG) are proposed to investigate the environmental and economic performances in summer and winter respectively. Our results reveal that the unit cost of transporting LNG via NSR is around 90 U.S. $ cents/GJ in summer and 120 $ cents/GJ in winter considering environmental cost. The findings also show that in the short run when regardless of environmental cost, HFO is still the preferable option for ships by its competitive cost. While when considering the environmental cost, LNG has better economic and environmental performance than other fuel types. So, for the long run, LNG might be encouraged to be the main fuel used in Arctic. [ABSTRACT FROM AUTHOR]

Traditional flat PV heat pumps has low irradiation density, large PV area, and the PV evaporators are always fixed, causing large area occupation and lots of irradiation missing, which limit the application and system efficiency. To solve the problems, a novel concentrated PV-air dual-source heat pump system (CPV-DSHP) was proposed in the previous paper, but the annual performance and economic comparison with flat PV-DSHP (FPV-DSHP) are still blank, which are extremely concerning to researchers. Besides, the paper also fills related content about the impact of irradiation composition on the system performance and economics, as well as the irradiation comparison on tracking CPV and fixed FPV surface. From the calculation of the validated model, it's found that the CPV-DSHP system could capture 6098 MJ and 2213 MJ more solar energy, and produce 282.21 kWh and 100.16 kWh more electricity in Garze and Hefei in a year. The annual concentration efficiency is 82.73 % in Garze where direct irradiation accounts for 68.89 %, which is only 73.15 % in Hefei with 39.84 %.The COP PVT is annually lifted by 0.40 in Garze. Although the two systems could both get payback in 5∼6 years, the CPV-DSHP only shows economic advantages in places with good direct irradiation conditions like Garze. • A novel concentrated PV-air dual-source heat pump system (CPV-DSHP) is presented. • The annual performance and economic comparison in different cities are supplemented. • The CPV-DSHP could produce 282.21 kWh more electricity in Garze in a year. • The annual concentration efficiency is 82.73 % in Garze with better direct irradiation. • Payback time is 5∼6 years but CPV-DSHP only shows economic advantage in Garze. [ABSTRACT FROM AUTHOR]

Forecasting international trade flows is crucial for understanding global economic activity and serving as critical economic indicators used by economists and policymakers with significant implications for respective countries' economic policies. However, this is a challenging task due to the complexity of the relationships between entities involved in international trade. While several approaches have been proposed, knowledge graph-based models have emerged as promising solutions for predicting international trade flows. In this paper, we introduce a synthetic triple-generation algorithm for enhancing downstream tasks in knowledge graph embeddings based on the graph complement. The algorithm identifies missing relationships between entities by exploiting the complement graph and generates high-quality synthetic triples that improve the accuracy of predictions. We validate the generated triples using several knowledge graphs embedding methods and computing metrics. To perform an initial result screening, an international trade scenario is explored, demonstrating the effectiveness of the proposed approach in enhancing the performance of knowledge graph-based models for predicting international trade flows. [ABSTRACT FROM AUTHOR]

In this paper, we look at the interaction of terrorism with immigrants' quality of life (measured by the foreign-born unemployment rate and globalization level) for OECD countries, and its impact on GDP per capita. We find strong evidence that GDP per capita is adversely affected by domestic terrorism. The magnitude of this effect is also substantial: at the sample mean, a one-standard-deviation increase in the number of domestic incidents is found to decrease GDP per capita between 5.7 % and 7.8 % of the sample average depending on the specification used. These results contrast with previous research which finds that transnational terrorism primarily affects these economic indicators. We find strong evidence that when we factor in the interaction of the foreign-born unemployment rate with either type of terrorism, an increase in the foreign-born unemployment rate decreases GDP per capita. On the policy front, we show that peace is valuable, and OECD countries will benefit by adopting policies to reduce the problem of terrorism. We also find that an increase in the foreign-born unemployment rate has a large negative impact on GDP per capita and policies that close the gap between foreign-born and native-born unemployment rates (for example, those aimed at reducing discrimination against immigrants) help the economy. [ABSTRACT FROM AUTHOR]

This paper examines the ordering of economic and financial prospects using a rescaled semivariance below the maximum (SVBMax). This indicator is consistent with stochastic dominance (SD) until order three, but is distinct from any expected utility framework, both in terms of construction and in terms of economic implications, despite an apparent formal similarity with the quadratic expected utility framework. SVBMax is able to deal with economic puzzles such as the restaurant puzzle or Rabin's paradox. SVBMax also provides a consistent alternative performance measure to popular measures such as the Sharpe ratio, the Omega ratio, or lower partial moments with exogenous thresholds. Compared to almost stochastic dominance, SVBMax is devoid of the drawbacks associated with the determination of violation areas. SVBMax can also replace the Aumann-Serrano and Foster-Hart riskiness indicators, which do not conform to third order SD. Further, SVBMax can provide a way of reaching an optimal diversification of stock portfolios. Besides, we also show that SVBMax is consistent with mean-variance-skewness preferences. [ABSTRACT FROM AUTHOR]

Policymakers imposed constraints on public life to contain the Covid-19 pandemic. At the same time, fiscal, monetary and macroprudential policies implemented a large range of expansionary measures to limit the economic consequences of the pandemic and stimulate recovery. In this paper, we assess the response of the equity market as a high-frequency indicator of economic activity to containment and stabilization policies for 29 European economies. We construct indicators of containment and stabilization policies and estimate a range of panel VAR models. The main results are threefold. First, we find that stock markets are highly responsive to containment and stabilization policies. We show that domestic fiscal policy, macroprudential policy as well as monetary policy support the recovery as reflected in the stock market. Second, expansionary fiscal policy conducted at the European level reduces rather raises stock prices. Third, we estimate the model over subsamples and show that the counter-intuitive stock market response to EU policies is driven by the responses in medium- and high-debt countries. These countries' stock markets are also particularly susceptible to monetary policy announcements. [ABSTRACT FROM AUTHOR]

Agroforestry Systems (AFS) are among the productive systems which have the potential of conciliation between food production, conservation of ecosystem services and even recovery of degraded areas. Several studies have helped to reduce uncertainty regarding the economic performance of these productive systems. However, many financial viability studies of AFS assume that farmers will have access both to finance and to market outlets to dispose of all produce, unrealistic assumptions for smallholders, especially in agrarian reform settlements. In addition, the time for AFS to pay for themselves could be a barrier to the adoption of this type of system by smallholders. This paper aims to show how an institutional innovation for marketing fresh food – such as Community Supported Agriculture (CSA) – can influence positively the adoption of agroforestry systems by smallholders. The research is based on a case study of an agroecological producer association in a Brazilian agrarian reform settlement and on a financial viability analysis of one AFS, considering two marketing outlet scenarios. The main contribution of this paper is to discuss the potentialities of the CSA marketing arrangement for smallholders outside the North American and European contexts, and within the agroforestry farming environment. In this arrangement, the farmer does not have to prioritize few and specific species because of their high commercial value. Therefore, it can foster a more systemic and ecological approach to farming. The financial indicators show that the most pessimistic CSA scenario is still superior to the most optimistic organic market scenario, in the context of marketing agroforestry products in the Brazilian Federal District. The economic security resulting from this institutional innovation underscores the potential for synergy between agroforestry systems and CSAs. • The investment and cost profile of Agroforestry Systems (AFS) represent a financial risk to smallholders. • Lack of access to financing and to outlet markets makes it more difficult for smallholders to adopt AFS. • Community Supported Agriculture (CSA) reduces risks and may increase financial viability associated with AFS adoption. • CSA increases smallholders' perception of economic security related to AFS. [ABSTRACT FROM AUTHOR]

In spite of the decreasing differences between rural and urban areas (e.g. in their economic structures) there are still several limitations that the rural world has to deal with. In a constantly changing world, rural development policies are a crucial issue to respond to current and potential future challenges of rural areas. They must therefore be flexible to reinforce or discard objectives as new challenges arise, enabling the necessary adaptations in the territories. All this justifies the call for a set of indicators that allow an accurate evaluation of these policies and programmes. This paper presents a set of demographic, economic, environmental and social welfare indicators that must be taken into account to assess rural development and related policies. Using the qualitative Delphi methodology, a group of experts analysed 88 proposed indicators, after which a set of 25 was selected based on their importance and on the consensus of the panel. The panel also estimated the weight of each of the four dimensions considered (population, social welfare, economy and environment) within an index to measure rural development. It has therefore been found that, in addition to the economy, social welfare is one of the areas that contributes most to the development of rural areas, thus contradicting "traditional" indices based solely on economic and demographic indicators. All this points to the need to evolve towards an index based on the results of this paper. • Rural areas still face problems that need diverse, adapted and adaptable RD policies. • RD policies must be measurable to determine whether objectives are being achieved. • Delphi method was used to design a set of indicators representing the different RD dimensions. • Indicators were selected by a panel of experts with diverse roles on RD and based on consensus. [ABSTRACT FROM AUTHOR]

The paper presents an empirical analysis of the impact of institutional reform policies and institutional quality on the economic growth of five Western Balkan countries (WB countries: Serbia, Montenegro, Bosnia and Herzegovina, Northern Macedonia and Albania) in the period 2006-2016. It was developed its own model of quantification concerning the impact of the most important indicators of the quality of institutions on the economic growth of these countries, which are in a delayed phase of transition and at some stage in the EU accession process. Achieving high and stable rates of economic growth for WB countries becomes the ultimate prerequisite for completing the EU transition and accession process. In order to improve growth dynamics, among other things, it is necessary to identify key drivers of growth and to model appropriate growth and development policies based on the results obtained. In the paper, WB countries were viewed as a whole. By empirically testing the impact of individual quality indicators of institutions on economic growth, according to the World Bank Governance Indicators methodology by using panel data multiple linear regression analysis, the largest statistically significant and positive impact came from the Government Effectiveness and Regulatory Quality variable. The intensity of the impact of the Control of Corruption and Rule of Law variable on GDP per capita is slightly weaker, but it is also very pronounced. In this respect, the empirical results obtained can be a useful framework for modeling the development policies of WB countries. They represent an important guide for policy makers to implement measures aimed at improving the quality of institutions and at the same time modeling economic growth policies. [ABSTRACT FROM AUTHOR]

• Conceptualization of the notion of 'sustainable' structural change. • Proposal for a methodology to mitigate government failures. • Construction of two composite indicators measuring economic and green performances. • Application to the analysis of the US manufacturing system. With the 2008 economic crisis–and again with the outbreak of the coronavirus emergency in 2020–industrial policy has explicitly reappeared in the political agenda of many countries and regions. A common feature has been the demand and the adoption of selective policies aimed at promoting specific targets of the economy, such as particular industries, technologies, companies, or territories. Nevertheless, the theoretical framework adopted to legitimize and implement public action in this field has not evolved much in the last two decades: it appears weak, too vague, and often anachronistic, increasing the risk of substantial 'government failures'. By focusing on the US manufacturing system, this paper contributes to the political economy stream of the industrial policy literature by proposing new methods to make government intervention more effective, efficient, and oriented towards a sustainable structural change, as defined in the paper. [ABSTRACT FROM AUTHOR]

Batch processes usually adopt a two-layer architecture control strategy to reduce the calculation cost while ensuring economic performance. The upper layer in the strategy obtains the nominal optimal reference trajectory by solving an economic optimization problem, while the lower MPC one possesses the task of tracking this trajectory as closely as possible. The disadvantage of this two-layer strategy lies in the lower layer resisting all the disturbance for satisfactory tracking results, regardless of the fact that part of the disturbance will be beneficial to economic performance. In order to realize the economic promotion in batch processes, a batch-to-batch optimization method is proposed in this paper. As an alternative to the traditional lower-layer tracking MPC, the proposed algorithm uses Gaussian process regression to establish a more accurate linear time-varying model and learns the information from the previous batches to obtain a more precise state estimation, thus continuously moving up economics by solving the economic optimization problem. Convergence of the proposed algorithm is proved and simulation is performed to verify the effectiveness of the proposed algorithm on economic performance improvement in comparison with the traditional tracking one. • An economic performance improvement strategy is proposed for batch processes. • Disturbances are utilized instead of being resisted to promote economy. • Gaussian process regression is utilized to compensate for model-plant mismatch. • Kalman filter is adopted to realize an accurate state prediction of the batches. [ABSTRACT FROM AUTHOR]

• Identification of important indicators for process safety. • A comparative study of process safety indicators in developing and developed countries. • Measuring the relative importance of the indicators using the Fuzzy Best Worst Method. Introduction: The importance of safety in high-risk industries such as oil and gas facilities has been reported previously. Process safety performance indicators can provide insight into improving the safety of process industries. This paper aims to rank the process safety indicators (metrics) by Fuzzy Best-Worst Method (FBWM) using the data gathered through a survey. Method: The study uses a structured approach considering the UK Health and Safety Executive (HSE), the Center for Chemical Process Safety (CCPS), and the IOGP (International Association of Oil and Gas Producers) recommendations and guidelines to generate an aggregate set of indicators. It calculates the level of importance of each indicator based on the opinions of experts from Iran and some Western countries. Results: The findings of the study demonstrate that some lagging indicators such as the number of times processes do not proceed as planned due to insufficient staff competence and the number of unexpected disruptions of the process due to failure in instrumentation and alarms are important in process industries in both Iran and Western countries. Western experts identified process safety incident severity rate as an important lagging indicator, whereas Iranian experts considered this as relatively unimportant. In addition, leading indicators such as sufficient process safety training and competency, the desired function of instrumentation and alarms, and proper management of fatigue risk play an important role in enhancing the safety performance of process industries. Experts in Iran viewed permit to work as an important leading indicator, while experts in the West focused on fatigue risk management. Practical Applications: The methodology used in the current study gives a good view to managers and safety professionals in regard to the most important indicators of process safety and allows them to focus more on important process safety indicators. [ABSTRACT FROM AUTHOR]

With the rapid popularization of mobile devices, the mobile crowdsourcing has become a hot topic in order to make full use of the resources of mobile devices. To achieve this goal, it is necessary to design an excellent incentive mechanism to encourage more mobile users to actively undertake crowdsourcing tasks, so as to achieve maximization of certain economic indicators. However, most of the reported incentive mechanisms in the existing literature adopt a centralized platform, which collects the bidding information from workers and task requesters. There is a risk of privacy exposure. In this paper, we design a decentralized auction framework where mobile workers are sellers and task requesters are buyers. This requires each participant to make its own local and independent decision, thereby avoiding centralized processing of task allocation and pricing. Both of them aim to maximize their utilities under the budget constraint. We theoretically prove that our proposed framework is individual rational, budget balanced, truthful, and computationally efficient, and then we conduct a group of numerical simulations to demonstrate its correctness and effectiveness. • We enriched the background in the introduction section. • We added a lot of the latest references. • We refined the language and descriptions to make it more clear. • We added many comparative experiments and figures. [ABSTRACT FROM AUTHOR]

• Evaluate the effect of uncertain operating and market conditions on dispatch planning. • Apply Model Predictive Control (MPC) for dispatch scheduling of CSP plants. • Monte-Carlo simulation is to evaluate the MPC performance for a case-study in Australia. • The plant profitability from electricity sales under spot prices is assessed. The aim at dispatch scheduling for concentrating solar power plants is to utilize thermal storage to maximize profit from electricity generation. The dispatching plan, however, must contend with significant uncertainty in both weather forecasts (particularly solar irradiation) and, in some locations, real-time electricity prices governed by potentially volatile markets. This paper proposes a Model-Predictive Control (MPC) which employs a novel similarity-based forecast for weather variables and exploits the predictions of electricity prices provided by the market operator. A Monte-Carlo simulation is developed to evaluate true performance of the proposed MPC against two benchmarks, with perfect knowledge (PK) forecasts and day-ahead optimized using prototypical weather (DAPW). A case study is performed on a 115 MW Solar Tower plant with 8-hour storage, hypothetically located in South Australia, and operates under either a fixed-price or a real-time spot price scenario. A Monte-Carlo simulation is conducted for 150 tests for January (summer) and August (winter). The results show in fixed-price scenario MPC achieves 82.4 % of optimal profit (i.e. obtained via PK) in January and 72.4 % in August whereas that in DAPW falls to 71.5 % and 56.9 % of optimal, respectively. In the spot market scenario, MPC reaches 71.3 % and 63.6 % of optimal profit in January and August, whereas that for DAPW reaches 61.4 % and 55.1 %, respectively. In conclusion, PK forecast assumption in dispatch planning over-estimates the achievable profit by ∼ 28–40 % particularly in spot market scenario. Moreover, MPC can mitigate the influence of uncertainties on the plant economic performance by 8.5 %–15.5 % compared to DAPW benchmarks. [ABSTRACT FROM AUTHOR]

There has been a strong wave of Eurobond issuances by Africa's frontier market economies since the start of the century. But it is not clear how these issuances have affected economic performance. This paper uses synthetic control experiments to conduct comparative case study analyses of the impacts of Eurobond issuances on economic growth, debt sustainability, and domestic capital markets. Ex post, we compare the trajectories of the relevant macroeconomic outcomes against their synthetically constructed business-as-usual counterfactual in an environment with no Eurobond issuance. The results show that, on average, sovereign Eurobond issuances have led to improvements in per capita GDP in Africa by about 10 percent above the counterfactual, business-as-usual scenario. Although most issuances were within 3 percent of GDP, they potentially led to about 13 percentage point acceleration in the debt-to-GDP ratios after ten years, compared to the counterfactual. Public capital accumulation is, on average, faster in the first two to three years following an issuance in countries with positive correlations. • Eurobond issuances by African sovereigns surged in the 2000s, with over 125 instruments issued by 21 African sovereigns. • We conduct synthetic control experiments, comparing relevant macro-outcomes with their counterfactuals under no issuance. • Eurobond have had a noticeable positive effect on incomes, about 10 percent annual improvement above the counterfactual. • But debt sustainability risks from Eurobond issuances are heightened by 13 percent above the counterfactual within 10 years. • Downside risks from Eurobonds should be actively managed to remain a useful source of financing and development in Africa. [ABSTRACT FROM AUTHOR]

Traditional empirical models analyze impact of sentiments on financial market volatility using macroeconomic fundamentals or financial indicators. In this paper, recent methods of text based sentiment analysis of market from relevant news articles regarding economy and financial market are used. Two distinct market sentiments namely, positive and negative sentiments are constructed using different emotions which are identified through standard natural language processing methods. Further, the paper aims at proposing an augmented version of asymmetric GARCH model of conditional volatility for Indian stock exchange, Sensex, during the time period of April 19, 2007 to January 10, 2020 by incorporating aforementioned market sentiments. Empirical findings suggest dominant impact of negative market sentiment over positive one and it also provides evidence of noise trading in financially immature Indian stock market. [ABSTRACT FROM AUTHOR]

Scaling up food security innovations in low-income rural environments has often failed to achieve substantive and lasting results. This poor performance can be attributed to dominant, linear approaches associated with spreading innovations which entail technology research and development and subsequent transfer to farmers. Such approaches tend to overlook complexity elements and non-linear processes in smallholder agriculture, including multiple stress factors such as climate variability and economic risks that make the uptake of new agricultural innovations more unpredictable. This article presents programmatic lessons from the Canadian International Food Security Research Fund (CIFSRF) on scaling up. It considers projects that: i) deployed successfully pilot-tested innovations to reach and benefit large numbers of beneficiaries; and projects that ii) used innovations as an entry point to catalyse systematic change in the food and agricultural sector. The paper also outlines several key scaling up principles that can encourage better understanding of relevant socio-ecological dynamics and complexities in intervention areas as a way to support innovations (at scale) that can contribute to more sustainable system outcomes. Finally, the paper reflects on how predominant definitions of impact at scale, centered around rather narrow indicators around economic growth and technology transfer, might consider more holistic goals that encompass integrated agricultural interventions that advance sustainable agri-food system outcomes. [ABSTRACT FROM AUTHOR]

Increasing pressure from governments and stakeholders has motivated the study of sustainability assessment in the supply chain context at operational, tactical, and strategic levels. Several papers have been published during the last two decades, and the number is still rising. Although several authors present complex models that include environmental and social assessment, the applicability and usefulness of these works is often limited by lack of data availability and lack of consensus in what is to be measured on implementations of sustainable practices and strategies. This paper presents a systematic literature review of works addressing the supply chain network design (SCND) problem, in which at least two of the three dimensions of sustainability are assessed. This paper aims to identify indicators that are used when sustainability is evaluated in real applied cases. A total of 113 papers from 2015 to 2018 were selected, including documents studying forward, reverse, and closed loop supply chains (CLSC). Indicators in the economic, environmental, and social dimensions were classified according to an existing framework in the sustainable supply chain literature. The review finds a highlighted emphasis on environmental considerations; social criteria are still hardly studied. The study country origin analysis also shows an increasing concern for sustainable practices in developing economies, mainly in Asia. Finally, this paper presents a brief description of the areas where research opportunities exist, including sectors, measures, and methodologies to assess sustainability in the SCND problem. [ABSTRACT FROM AUTHOR]

Indicators of technological emergence promise valuable intelligence to those determining R&D priorities. We present an implemented algorithm to calculate emergence scores for topical terms from abstract record sets. We offer a family of emergence indicators deriving from those scores. Primary emergence indicators identify "hot topic" terms. We then use those to generate secondary indicators that reflect organizations, countries, or authors especially active at frontiers in a target R&D domain. We also flag abstract records (papers or patents) rich in emergent technology content, and we score research fields on relative degree of emergence. This paper presents illustrative results for example topics – Nano-Enabled Drug Delivery, Non-Linear Programming, Dye Sensitized Solar Cells, and Big Data. • We present a routine to calculate R&D emergence scores for topics in a dataset. • Emergence scores distinguish cutting-edge topical activity in a research domain. • We measure cutting-edge R&D organizations, countries or authors. • Emergent terms for 3 case analyses predict succeeding period R&D activity. • Emergence scoring spotlights cutting-edge R&D organizations in the 3 cases. [ABSTRACT FROM AUTHOR]

This paper uses a case study of Cornwall to address the question of what new politics of place that counter-urbanisation brings about. Counter-urbanisation to Cornwall in the SW of the UK has been popular for decades. Initially it was perceived as a way to repopulate this remote rural region, and challenge its sparse population distribution. Later it became a neo-endogenous growth tool to attract a creative class of dynamic entrepreneurs, and the population has steadily grown since the 1960s. However, Cornwall remains one of the poorest parts of the UK, and poverty and homelessness are endemic. This paper uses ethnographic data to explore the social and economic impact that this has had on a coastal village, the ways that newer residents are socialized into the community, and how the village has responded to challenges maintaining a vibrant village life. The paper will use the concept of the affective assemblage to explore the different imaginaries that participants hold about Cornwall in general and in the village in particular, as it tries to maintain a cohesive community during processes of rural gentrification. The paper uses the concept of the complex adaptive assemblage to show that although the village will remain over time, the question is about whether the community will be liveable for existing inhabitants, and for those who find themselves vulnerable. Furthermore, although it has raised some economic indicators such as the cost of housing, there has been no subsequent improvement in the economy of Cornwall as a whole, and many of the participants in this study discussed how their move was facilitated by their ability to work remotely, generating productivity elsewhere rather than locally. [ABSTRACT FROM AUTHOR]

In the digital era, digitalization brings great opportunities and challenges for cities' environmental governance and economic growth. However, it is worth noting that the impact of digitalization on cities' green transition has not been studied from the perspective of Metcalfe's Law. This paper uses the principal component method and non-radial directional distance function (NDDF) to calculate the digital development level and the green economic performance of 269 Chinese cities from 2003 to 2019. (1) The empirical findings demonstrate that digitalization has become a strong driver for cities' green economic transformation. (2) Digitalization indirectly promotes green transition through industrial structure optimization and green technological innovation. (3) The heterogeneity results show that the positive effect of digitalization on green development is more obvious in eastern and economically developed cities. (4) Using the partially linear functional-coefficient model (PLFC), this paper studies the non-linear nexus between digitalization and green economic transition from the perspective of Metcalfe's Law. Thus, Metcalfe's law is confirmed in the green transition of Chinese cities. Some policy recommendations are offered for realizing the coordinated development of digitalization and greening of the urban economy. [ABSTRACT FROM AUTHOR]

• With optimal market strategy and DTR, the annual revenue may be 204% of a base case. • Ideal power forecast may not correspond to the best commitment strategy. • Depending on market prices, NPV may be between −441 million € and +9.5 billion €. • Economic benefits of DTR are estimated between 0.7 and 1 billion euros. • Overplanting and DTR may reduce LCOE by 1 €/MWh. The overplanting of an offshore wind farm (OWF) with dynamic thermal ratings (DTR) is a promising solution for enhancing OWF performance. As the overplanted OWF generates an additional energy and DTR ensures its better transfer, the research question is if final OWF profits would be higher than associated costs. While there has been growing attention on this subject in recent years, there is still no research investigating the commitment strategies of overplanted OWFs with DTR. This paper investigates how commitment strategies may affect the economic performance of an overplanted OWF with DTR. The results show that, depending on the day-ahead commitment strategy, the annual revenue of OWFs may theoretically increase by up to 21% even without overplanting nor DTR, and by up to 204 % with overplanting and DTR. However, although commitment strategies, overplanting and DTR may significantly increase annual revenues of an OWF, its net present value (NPV) still heavily depends on market prices. In the presence of low market prices as in 2018, overplanting actually reduces the NPV of an OWF and, under the conditions considered here, keeps its NPV always negative. On the contrary, in the presence of high market prices as in 2022 or feed-in tariffs, the overplanting increases the OWF NPV. In the latter case, the NPV of the overplanted OWF is estimated between 1.5 billion and 9.5 billion euros while the discounted payback period is 3–12 years. The economic benefits of DTR for the overplanted OWF are estimated between 0.7 and 1 billion euros. The paper also shows that the optimal overplanting rate is very different whether the NPV or LCOE are considered. Finally, the paper shows that committing to the actual OWF power production (i.e. assuming a perfect power forecast) does not necessarily result in the highest revenue. [ABSTRACT FROM AUTHOR]

The high cost of operation and maintenance can impede the development of offshore wind turbines (OWTs). With advancements in detection technology, condition-based maintenance (CBM) has emerged as a promising approach to managing maintenance. Numerous CBM polices have been studied to achieve a cost-effective maintenance. However, little research has been conducted on discovering the cost-optimal CBM policy for OWTs. With this motivation, a deep reinforcement learning (DRL) framework for exploring the cost-optimal CBM policy is proposed in this paper. Four CBM policies (i.e. uniform or dynamic inspection interval, and fixed or adaptive repair threshold) are formulated as the Markov decision process model. Two DRL algorithms (deep Q network and proximal policy optimization) are applied to derive the dynamic inspection interval and the adaptive repair threshold. To illustrate this framework, a fatigue OWT component is used as an example. The four policies are optimized under varying conditions to find the cost-optimal CBM policy. Moreover, performance comparison of DRL algorithms is performed. The case study illustrates the advantage of DRL in deriving optimal maintenance policies for fatigue-sensitive OWT components and highlights the interrelationship between maintenance policy and economic performance. This paper concludes by discussing the effect of cost elements on the CBM policies. • A DRL framework is proposed to derive the cost-optimal CBM policy for OWTs. • CBM policies with different inspection intervals and repair thresholds are studied. • The performance of two DRL algorithms is compared under different conditions. [ABSTRACT FROM AUTHOR]

In a companion paper, Ojeda-Rojas et al. (2021) [1] describe a stochastic agent-based simulation (ABS) model of a cow-calf operation on a commercial farm in São Paulo, Brazil. The model's parameterization was based on data collected from two sources: a real beef cattle herd and related scientific literature. Based on the mentioned simulation model, this study aims to assess the economic outcome of 10 different reproductive scenarios: Natural mating only (ONM); one timed artificial insemination (TAI) plus natural mating (NM) (1TAI + NM); two TAI plus NM, with 24, 32, and 40 days between TAI (2TAI/24 + NM, 2TAI/32 + NM, and 2TAI/40 + NM, respectively); three TAI without NM, with 24, 32, and 40 days between TAI (3TAI/24, 3TAI/32, and 3TAI/40, respectively); and three TAI plus NM, with an interval between TAI of 24 (3TAI/24 + NM) and 32 days (3TAI/32 + NM). The simulation was performed on an animal-by-animal basis over a time horizon of 5000 days. Each scenario had 32 farms, and each farm kept up to 400 adult females. According to the scenario, a bull population was composed of 0, 7, or 15 individuals. The outcomes, represented as means ± standard deviations, were assessed after reaching a steady-state (1825 days). The model outcomes showed that the 3TAI/24 + NM scenario resulted in higher incomes (US$ 96,479.19 ± 709.81), whereas the ONM scenario had the lowest incomes (US$ 79,753.37 ± 741.87). The 3TAI/24 + NM (US$ 101.720.63 ± 79.21) and ONM (US$ 90.898.58 ± 59.17) scenarios presented the highest and lowest total operating costs (TOC), respectively. However, when TOC was evaluated per kg of the weaned calf, the highest and lowest costs were associated with the ONM (US$ 2.81 ± 0.03/kg) and 2TAI/24 + NM (US$ 2.17 ± 0,04/kg) scenarios, respectively. Our model suggests that reproductive strategies that use TAI have a better economic performance than those under NM. However, when performing three TAI with an interval of 40 days, the benefit was lower; in some cases, it was even worse than the ONM. Combining TAI with early pregnancy diagnosis resulted in better economic performance than other TAI programs and NM. The 2TAI/24 + NM scenario outperformed the others due to the contrast between its high income and moderate costs. Beef cattle production is a highly complex system. Simulations models, specifically ABS models, could make the decision-making process on complex systems straightforward and effective. Furthermore, ABS models can overcome the limitations of conventional research approaches, such as high costs and long experimentation periods. • A stochastic agent-based model was used to simulate a cow-calf operation. • We evaluate the economic performance of using natural mating alone or with TAI. • The analysis considers the number of TAIs (1, 2, or 3), days between them (24, 32, and 40), and early pregnancy diagnosis. • Combining TAI with early pregnancy diagnosis resulted in a better economic performance. • The 2TAI/24 + NM scenario outperformed the others due to its high income and moderate costs. [ABSTRACT FROM AUTHOR]

• We present a multinomial approach not previously used in the macroeconomic literature for the study of growth and industrial dynamic. • The successful growth dynamic observed during the 1995–2014 period in several emerging economies is very heterogeneous in terms of their industrial transformation. • Developed economies grew slowly and, generally, through episodes of limited industrial transformation. • The relationship between economic fitness and the countries' industrial and growth dynamics generates a path dependency in economic development. • The fitness score of a country –that moves very slowly during one or two decades– establishes the most common sequences of episodes to be observed. When analyzing countries' medium- and long-term economic performance, it is important to study jointly the dynamic of growth and the industrial evolution that determines how the productive structure changes over time. In this paper, we use the Economic Fitness metric to describe the competitiveness of the countries' industrial structure, and classify economic episodes, with five-years windows, to establish how countries grow (i.e., above or below a trend, and with a dynamic or static industrial structure.) We show a complex growth dynamic using data covering two decades (1995–2014) for a large set of countries. This pattern indicates that the observed sequences of spells vary substantially even between countries within the same growth regime (low, medium, and high). Moreover, we find a robust statistical relationship between these spells and Economic Fitness with a multinomial econometric model. In particular, we show that economies with higher fitness are more resilient since episodes of below-average growth and the net disappearance of competitive firms are less likely to happen. [ABSTRACT FROM AUTHOR]

• We assess the sustainability of growth led by government and exports demand. • We verify the stability of public and external debt ratios in an SSM model. • Thirlwall's Law appears as the external constraint growth rate in the long run. • A fiscal policy rule changes the government's share to contain foreign debt. • Simulations suggest the economic performance improves with structural policies. The Sraffian supermultiplier model revealed the role of autonomous demand in economic growth. Nevertheless, the sustainability of growth requires the stability of the financial stocks behind autonomous demand. Growth led by government expenditure and exports is thus stable if both public and external debts are stable. In this paper, we assess the stability of this growth process in a supermultiplier model. The external constraint may appear as a restriction to external indebtedness, imposing an upper limit to growth. Nonetheless, the domestic autonomous expenditure can keep the economy distant from the external constraint. A fiscal policy rule is proposed to contain foreign indebtedness. Simulations support the analytical results, and compare the outcomes of the fiscal policy rule and a structural change policy. Altogether, the model provides stability conditions for growth in an open economy paying its international liabilities in foreign currency. [ABSTRACT FROM AUTHOR]

Internet satellite constellations are expected to play an important role in accommodating the rising global demand for internet access. Such rise in demand, however, is highly uncertain. Staged deployment is an approach that provides flexibility to tackle demand uncertainty by enabling the real option to reconfigure a constellation if demand changes. Advancements in satellite technology have led to the emergence of multi-layered constellations. This opens the opportunity to enhance staged deployment by enabling an additional real option: adding a new layer to a constellation. This real option has no associated reconfiguration costs, and therefore has the potential to reduce the cost of staged systems deployment. This paper proposes a framework to design multi-layer staged deployment systems and analyse their effectiveness in modern mega-constellations under global demand uncertainty. The framework is applied to four case studies based on market projections. Results show that multi-layer staged deployment decreases the expected life-cycle cost (ELCC) by 42.8% compared to optimal traditional single-layer deployment. Multi-layer staged deployment is more cost effective than single-layer staged deployment in all practical cases, which decreases ELCC by 22.9% compared to traditional deployment. Several cost altering mechanisms in staged deployment are identified. The results and analysis provide improved economic performance and better resource utilization, thus contributing in the long term to improved sustainability and market resilience. An accompanying decision support system provides system engineers with valuable insights on how to reduce deployment costs using the proposed multi-layered staged strategy. • Staged mega-satellite constellations deployment provides flexibility to deal with uncertain growing global internet demand. • A proposed framework enables more adaptable, sustainable, and resilient multi-layer staged deployment (MLSD) systems. • Results show 43.1% decrease in expected life-cycle cost (ELCC) compared to traditional design strategies. • Flexible MLSD is 22.9% more cost effective than flexible single-layer staged deployment. • A decision support system provides further insights on design and deployment of flexible MLSD systems. [ABSTRACT FROM AUTHOR]

Recent technological developments make perovskite solar cells (PSCs) particularly suitable for building integrated photovoltaic (BIPV) applications on vertical building envelopes, but the relatively short lifespan of PSCs requires frequent replacement, thereby generating substantial waste. Careful consideration of circularity credentials of this novel technology is therefore essential prior to the extensive implementation of vertical PSCs in building envelopes. This paper provides a circular economy approach for the implementation of PSCs in vertical envelopes and assesses its economic feasibility by life cycle cost analysis in Europe. The process of recycling PSC is developed. The economic performance of PSC envelopes is provided and compared to that of the conventional rigid BIPV system. Uncertainty analysis and sensitivity analysis of economic indicators are then performed to identify the influential parameters. The findings indicate that the PSC envelope has a significant potential for circular BIPV components and that PSCs applied on vertical envelope are economically viable. • LCCA of vertical PSC envelopes with recycling strategy is analyzed in Europe. • Recycling strategy of PSC is explored before diffusion to overcome PSC's short lifespan. • PSC price is the most influential factor for the deviation of LCOE. • PSC envelope has potential to achieve profitability in most regions of Europe. [ABSTRACT FROM AUTHOR]