Input Output Research Articles

ESG performance, corporate innovation and downside risk: empirical evidence from China

PurposeThis study mainly explores how ESG performance (ESG stands for Environment, Social, and Governance) affects corporate downside risk through innovation input and innovation output, thereby promoting sustainable development of enterprises.Design/methodology/approachUsing Chinese A-share listed companies from 2014 to 2022 as research samples, a stepwise regression method is used to empirically test the impact of ESG performance on corporate innovation and downside risk by constructing multiple multivariate primary regression models.FindingsESG performance is beneficial for obtaining external resources and alleviating principal-agent problems. It can promote enterprises to increase innovation input and improve innovation output, thereby enhancing their core competitiveness, and suppressing their downside risk. This inhibitory effect is more significant in non-state-owned enterprises, non-high-tech enterprises, and enterprises where the chairman and the general manager are not combined in one. Further additional analysis has found that equity concentration weakens the inhibitory effect of ESG performance on corporate downside risk, equity balance strengthens the inhibitory effect of ESG performance on corporate downside risk, indicating that a mutually restrictive equity structure is conducive to promoting enterprises to actively fulfill ESG responsibility, thereby improving corporate innovation level and resolving their downside risk.Practical implicationsEnterprise managers, policy makers, and other practitioners can clearly see the benefits of implementing ESG measures, further strengthen their confidence in sustainable development, actively apply ESG concepts to the entire production and operation process of enterprises, increase attention and implementation of ESG elements, and promote the healthy and vigorous development of enterprises and macroeconomics.Originality/valueThe research conclusions reveal the inherent mechanism by which ESG performance empowers enterprises to improve their innovation level and reverse their performance decline, effectively expanding the theoretical achievements of ESG performance in enterprise innovation and risk management.

Intuitionistic fuzzy three-way decision method based on data envelopment analysis

As a powerful mathematical tool for tackling uncertain decision problems, three-way decision has garnered substantial attention since its inception. However, real-world decision problems are inherently complex, and decision-makers often exhibit characteristics of incomplete rationality. Traditional three-way decision models, which rely on functions or relationships, face challenges when confronted with multi-output problems. In response to this challenge, this paper introduces an intuitionistic fuzzy three-way decision model grounded in data envelopment analysis (DEA). Initially, we propose an input–output correlation degree that integrates hesitancy information and serves as a procedural indicator for benefit scores. Subsequently, the traditional DEA is extended to accommodate the intuitionistic fuzzy environment and utilized to construct a comprehensive loss function. Furthermore, a novel intuitionistic fuzzy three-way decision model is developed, incorporating three dimensions: optimism, neutrality, and pessimism, and corresponding decision rules and algorithms are provided. Finally, the effectiveness of the proposed model is rigorously validated through a series of experiments and comparative analyses. The model offers a pioneering approach to address uncertain multi-input–output decision problems, effectively integrating decision-maker’s risk preferences within an intuitionistic fuzzy environment.

The Optimization and Control of the Engagement Pressure for a Helicopter Dry Clutch

The engagement quality of a helicopter dry clutch has a significant impact on the service life and overall flight performance of the helicopter. The engagement oil pressure is an important factor affecting the clutch engagement quality. Firstly, a nonlinear input–output dynamic model for the dry clutch is developed to investigate the optimization and control of dry clutch engagement pressure in this paper. Secondly, to efficiently obtain the optimal pressure curve, an optimal method combining the developed dynamic model with the state feedback gain of a linear quadratic optimization regulator (LQR) solver is proposed. Thirdly, considering that hydraulic actuators may struggle with tracking certain pressure curves, a hydraulic actuator for accurately tracking pressure curves based on fuzzy PID is proposed. The simulation results indicate that the developed hydraulic actuator exhibits an excellent tracking performance. Moreover, compared with linear and segmented pressure curves, the optimal pressure curve derived from the proposed method significantly reduces jerk, friction work, and engagement duration, resulting in improved helicopter dry clutch engagement quality.

A Life Cycle and Economic Assessment of the Peggy Guggenheim Collection in Venice for Environmental and Economic Sustainability

This paper applies selected methodologies for the measurement of the environmental and economic sustainability of the Peggy Guggenheim Collection (PGC) in Venice with a view to assessing the PGC’s sustainability and commitment to implementing selected SDGs. To assess environmental sustainability, a life cycle assessment (LCA) has been carried out. The museum is conceptualized as a “firm” that produces several outputs and needs several inputs. The results provide the number of annual CO2e (and other pollutants) emissions linked to the regular activity of the museum. The environmental cost (in EUR), linked to the impacts obtained from LCA, has been calculated. To assess economic sustainability, a survey and econometric methods were used to value services directly generated by the museum, and input/output methods were used to compute the direct and indirect impacts on the local economy. Nonetheless, PGC visitors (those who travel to Venice with the main objective of visiting the PGC) contribute to around 1.2%/1.4% of Venice’s GDP. The results from input–output tables show that, although the final demand generated by the PGC’s own activities amounted to about EUR 620 million in 2022, the economic benefits of the PGC beyond this final demand are significant and very positive due to carry-over effects. Specifically, the PGC leads to an increase in GDP of around EUR 1.200 million, with a multiplier of 1.9. In terms of employment, around 8200 jobs are associated with the presence of the PGC. The net public finance revenue also clearly benefits, with a net income of around EUR 150 million in 2022. Comparing both the environmental and economic impacts of the PGC, one can conclude that the annual activities performed by the museum are highly sustainable, with the economic pillar strongly offsetting the costs generated using natural resources. The creation of economic value, therefore, is generated in respect of environmental boundaries, even if some minor flaws can be highlighted. The connection between museums and sustainable development goals is highly recognized. The findings show the PGC’s commitment to achieving and implementing selected SDGs, including SDG 4, SDG 11, and SDG 16, by implementing actions and strategies that are aligned with these goals.

An Efficiency Evaluation and Driving Effect Analysis of the Green Transformation of the Thermal Power Industrial Chain: Evidence Based on Impacts and Challenges in China

The high carbon emissions and pollution of China’s thermal power industry chain have exacerbated environmental and climate degradation. Therefore, accelerating the green transformation process is of great significance in promoting the sustainable development of enterprises. This study selected 30 listed thermal power enterprises in China as research objects, analyzed their data from 2018 to 2022, set targeted input–output indicators for different stages, and used a two-stage dynamic data envelopment analysis (DEA) model to evaluate and measure the efficiency of the green transformation of Chinese thermal power enterprises. In addition, this study also uses the logarithmic mean Divisia index (LMDI) method to analyze the driving effects of green transformation. The results indicate that in terms of overall efficiency, there is a significant difference in the overall performance of these 30 thermal power enterprises, with a large difference in average efficiency values. Efficiency values are related to enterprise size. In terms of stage efficiency, the average efficiency value of thermal power enterprises in the profit stage was significantly higher than that in the transformation stage, and the profitability of Chinese thermal power enterprises was better. In terms of sub-indicator efficiency, the efficiency of each indicator shows a “U”-shaped trend, and there is a certain correlation between the operating costs and revenue of thermal power enterprises, the market value of green transformation, and related indicators. In addition, the most important factor affecting the efficiency of green transformation is the sewage cost they face, whereas their operational capabilities have the least impact on their green transformation. In this regard, thermal power enterprises should increase their investment in the research and development of key technologies for thermal power transformation and continuously optimize their energy structure. The government will increase financial support for thermal power green transformation enterprises and correspondingly increase emission costs.

Design and analysis of flexible tilt mirror driven by piezoelectric thin sheet actuators

Optical tilt mirrors (OTM) enable the mirrors to achieve high precision posture adjustment, playing a significant role in various optical system applications in the fields such as active optics, adaptive optics, satellite communications and biomedicines. However, the rapid development of modern technologies has brought new challenges to OTM in terms of their dimension, operating space and dynamic performance. Herein, a novel biaxial optical flexible tilt mirror (OFTM) driven by four simple bonded-type thin sheet piezoelectric actuators was proposed, where the OFTM was designed based on the flexible bending motions of composite piezoelectric beams. Particularly, OFTM is mainly composed of PZT sheet and stainless steel substrate, and shows a dimension of 42 mm × 42 mm × 0.2 mm. A theoretical model of OFTM was established to accurately describe the relationship between input electric field and output pose based on elastic beam theory, where the relationship was verified by both finite element analysis and experimental results. Finally, an OFTM prototype was fabricated and presented good performance on motion behaviors. Compared with traditional fast steering mirror (FSM) and MEMS mirror, the developed OFTM shows low cost, small size and simple manufacturing process, which greatly extends the applications of OFTTM in emerging fields.

NeVer2: learning and verification of neural networks

AbstractNeVer2 is an open-source, cross-platform tool aimed at designing, training, and verifying neural networks. It seamlessly integrates popular learning libraries with our verification backend, offering their functionalities via a graphical interface. Users can design the structure of a neural network by intuitively arranging blocks on a canvas. Subsequently, network training involves specifying dataset sources and hyperparameters through dialog boxes. After training, the verification process entails two steps: (i) incorporating input preconditions and output postconditions via dedicated blocks, and (ii) initiating verification with a simple “push-button” action. To our knowledge, there is currently no other publicly available tool that encompasses all these features. In this paper, we present a comprehensive description of NeVer2, illustrating its complete integration of design, training, and verification through examples. Additionally, we conduct experimental analyses on various verification benchmarks to illustrate the trade-off between completeness and computability using different algorithms. We also include a comparison with state-of-the-art tools such as $$\alpha $$ α ,$$\beta $$ β -CROWN and NNV for reference.

Nonlinear optical encoding enabled by recurrent linear scattering

AbstractOptical information processing and computing can potentially offer enhanced performance, scalability and energy efficiency. However, achieving nonlinearity—a critical component of computation—remains challenging in the optical domain. Here we introduce a design that leverages a multiple-scattering cavity to passively induce optical nonlinear random mapping with a continuous-wave laser at a low power. Each scattering event effectively mixes information from different areas of a spatial light modulator, resulting in a highly nonlinear mapping between the input data and output pattern. We demonstrate that our design retains vital information even when the readout dimensionality is reduced, thereby enabling optical data compression. This capability allows our optical platforms to offer efficient optical information processing solutions across applications. We demonstrate our design’s efficacy across tasks, including classification, image reconstruction, keypoint detection and object detection, all of which are achieved through optical data compression combined with a digital decoder. In particular, high performance at extreme compression ratios is observed in real-time pedestrian detection. Our findings open pathways for novel algorithms and unconventional architectural designs for optical computing.

Power quality improvement of unipolar-input-bipolar-output DC transmission system via load power balancing

In DC transmission and distribution systems, both unipolar and bipolar transmission modes exist, and DC transformers used in these systems are also available in either unipolar or bipolar configurations. In actual systems, due to requirements such as economy, land occupation, and reliability, there is a tendency to use a system with unipolar input and bipolar output. However, the bipolar loads, if unbalanced, will lead to increased equipment costs and voltage imbalance, causing power quality problems. This paper defines the Power Unbalance Factor (PUF) to describe the power quality of the studied DC transmission system and presents an improved DC transformer topology based on a power balancing system. This topology realizes bipolar voltage balance and improves the power quality of the DC transmission system when the load is unbalanced. The influence of the proposed solution on the power design of the DC system is demonstrated through theoretical analysis, and its effectiveness for improving the DC power quality is verified by both simulations in MATLAB/Simulink environment and physical experiments. When the power electronic transformer needs to be overloaded, the proposed topology can reduce the design power of the two branches by using the difference power, which is economical.

How does the development of digital economy in central cities promote the coordinated development of regions? Evidence from 19 urban agglomerations in China

Externality is the power source of the spillover effect of digital economy development in China's central cities. Based on panel data of 200 prefecture-level cities in 19 urban agglomerations in China from 2011 to 2020, this research constructs a theoretical model with income output, labor, and capital input and adopts the panel fixed effect model to empirically test the impact of central cities' digital economy development on peripheral cities' urban agglomerations. The results are as follows. First, digital economy development in central cities significantly improves the level of such development in peripheral cities. Second, the spillover effect of this development in central cities is affected by their own geographical location, economic development level, and city size. Third, the spillover effect of digital economy development in central cities mainly arises through the learning mechanism.

Carbon‐trade nexus: The embodied carbon emission transfer network of China's sustainable development demonstration belt

AbstractUncovering energy‐related carbon emission transfer networks and assessing its structure and efficiency can help reshape the regional carbon‐trade nexus. Combining the social network analysis and interregional input–output model, our study estimates the carbon trade patterns from various perspectives and explores the topological characteristics of energy‐related carbon emission transfer networks within China's green development demonstration belt. Furthermore, carbon pressure index is used to identify key area for the regional carbon reduction. Results indicate that the three reaches are in descending order of embodied carbon emissions whether from the production or consumption perspectives when most of provinces are in the status of “carbon overload.” Furthermore, the total embodied carbon transfers with the large amounts occur at the provinces that are adjacent to each other. In terms of degree centrality, Jiangsu, Anhui, and Zhejiang dominate the carbon emission transfer network within the region, whereas Jiangxi, Sichuan, and Zhejiang serve as the main intermediaries and bridges. As a result, the findings should provide policymakers with new insights into how to formulate more targeted strategies for carbon management in different provinces and sectors.

Optimisation of the Circular Economy Based on the Resource Circulation Equation

The lack of effective evaluation methods and implementation guidelines has led to frequent obstacles in the process of circular economy in enterprises. The efficiency equation for resource circulation can effectively evaluate the efficiency of an enterprise’s circular economy resource circulation from three perspectives: input, circulation, and output. Additionally, it delves into each link to identify weak points, offering guidance for optimising the enterprise’s circular economy. Utilising a value flow analysis within the context of a circular economy, this paper evaluates circular economy efficiency using a resource circulation efficiency equation. It conducts factor analysis across three dimensions: resource input, resource circulation, and waste output. This analysis aims to evaluate the corresponding resource productivity, added value output rate, and environmental efficiency. Factor decomposition techniques were then employed to identify the underlying factors contributing to poor circular economy outcomes. Furthermore, based on the relationships among three resource circulation indicators, this paper forecasts the potential advantages of integrating circular economy improvement measures and proposes practical optimisation approaches. The enhanced resource circulation efficiency resulting from the proposed optimisation approaches was validated through a case study with an aluminium company.

Inverse Class‐E power amplifier with broadband capability at different switch‐off duty ratio

AbstractThe paper explores the investigation of an inverse Class‐E amplifier featuring a series output filter across various switch‐off duty ratios D. Analysis of different duty ratios as a design parameter reveals their impact on peak switch voltage, output power capability, and maximum operating frequency. Notably, it is demonstrated that adjusting the D ratio affects these parameters, with specific emphasis on achieving a maximum normalized switch voltage lower than 2 and an output power capability exceeding 0.1 for D = 0.7. Furthermore, the paper considers both parasitic shunt capacitance and series inductor in the load network, a departure from previous works that solely focused on the series inductor. The proposed circuit is highlighted for its ease of implementation compared with conventional reactance compensation circuits employing parallel resonant circuits, which are challenging to form directly. An innovative approach is introduced to showcase the broadband performance of the inverse Class‐E amplifier. The measured drain efficiency and output power versus input power at 430 MHz are 82% and 45.3 dBm, respectively. A similar performance can be achieved within the frequency range of 380–600 MHz by proper tuning at saturated power. The measurement results demonstrate a maximum high power‐added efficiency (PAE) of 79% and drain efficiency of 82% within this frequency range, accompanied by a gain exceeding 12.0 dB and output power surpassing 44 dBm.

Estimating inter-regional trade: the radiation model versus the gravity model

The estimation of interregional trade flows within a country is confined to the context of multi-regional input–output models. Despite the remarkable relevance of these trade flows for regional incomes, most countries have almost no official data, with Korea being an exception used here. In addition to survey-based estimation methods, there are non-survey methods, some based on gravity models, which can be used to estimate inter-regional trade flows. This paper compares the gravity model with the novel radiation model. Preliminary results show that the accuracy of the radiation model’s estimation is superior to that of the gravity model, with the invaluable benefit of not needing to estimate regression coefficients, unlike the gravity model. This finding provides a generalisation of the radiation model for estimating interregional trade in contexts where there is no prior multi-regional input–output table without restrictions derived from coefficient estimation as in the gravity model.

Heterogeneity analysis of food supply-demand patterns and land carbon loss under different dietary structures in China

China’s diets are undergoing a profound transformation, and carbon storage loss generated by dietary structure change is increasingly serious, accompanied by concerns about sustainable diets and food supply–demand conflicts. This paper designed a dual ‘material–environmental’ framework based on the dietary structure classification, around which we conducted the heterogeneity analysis of food supply–demand patterns and land carbon loss, and then tracked hotspot diets of trade-generated carbon loss through the multi-regional input–output (MRIO) model. The research found that 31 provinces could be classified into five clusters with obvious spatial agglomeration patterns, most of which still had a high proportion of staches, but also showed an evolutionary trend towards diversified compositions. Driven by dietary structure, the national food supply–demand match under the material accessibility dimension showed barely balanced overall, while in terms of environmental effects, ecosystems lost significant amounts of carbon stocks, with losses of 331 and 950 Tg11Tg refers to teragram, a weight unit, 1 Tg = 106 t = 1012 g of vegetation carbon and soil organic carbon, respectively, which together accounted for 14 percent of the total carbon stocks in the vegetation and topsoil carbon pools. The clusters to which Henan, Jiangsu, Zhejiang and Guangdong belong were tracked as hot dietary clusters exhibiting hotspots in inter-regional trade, contributing to carbon loss in external provinces through trade linkages. Heilongjiang and Inner Mongolia were at a low level of supply–demand balance but played the role of a carbon storage loser, while Guangdong, Zhejiang, and Jiangsu not only possessed higher balance levels but also pulled more external carbon loss. China's food system should optimise internal cropping structures and monitor external land use conversion. It is equally important to track food consumption side rights and responsibilities.

How important is the blue economy for regional development? – The case of Poland

Since 2012, the European Union has actively promoted the growth of its maritime economy, driven by factors such as climate change and energy shortages. Despite this, research on the blue economy's impact on subnational development remains limited, often focusing solely on coastal regions. This paper aims to explore the broader spatial effects of the maritime economy in Poland, a country with a diverse and rapidly growing blue economy. Our study employs an estimated interregional version of Leontief's input-output (I-O) model, focusing on Poland's 16 regions, including three coastal regions. We address two key methodological challenges: compiling maritime sectors within the I-O table and extending the national I-O table to an interregional dimension using a spatial econometric approach.Our research demonstrates the extensive reach of the blue economy across Poland, offering a comprehensive view of its direct and indirect impacts. The results reveal significant intersectoral and interregional linkages driven by maritime economic activities. These linkages extend beyond coastal regions, highlighting the blue economy's wider influence on regional development. By examining the demand for intermediate goods and services by maritime sectors across regions, we identify the spill-over effects that transcend regional boundaries. This knowledge is crucial for crafting policies that support both maritime industries and the regions supplying these industries, ensuring balanced and inclusive economic growth. Our findings provide specific insights for policymaking, suggesting targeted spatial policy interventions to enhance the effectiveness of maritime policy and its integration with regional development strategies.

Coupled analysis of arable land input intensity and output intensity based on sliding windows

Sustainable intensification (SI) of agriculture can produce more food to meet the demand of a growing population while considering ecosystem health. The current SI estimation framework ignores the complex coupling between input and output intensity of arable land. A method for coupled analysis of arable land input intensity and output intensity based on sliding windows is proposed. By calculating the correlation coefficient and partial correlation coefficient between input intensity and output intensity in different value ranges as the order parameter, the phase transition and the influence process of input intensity on output intensity can be explained. Meanwhile, a python-based framework is developed.An application of the method was made to reveal the interaction process between annual provincial input intensity and output intensity in mainland China. Researchers in many fields may benefit from the method by obtaining a fast way to analysis the coupling relationship between driving and dependent variables in complex systems.• New method for SI estimation is presented.• The order parameter of the coupling relationship between input and output intensity is calculated based on sliding windows.• Analysis of coupling relationships between driving and dependent variables in complex systems.

Developing Water Quality Formulations for a Semi-Distributed Rainfall–Runoff Model

Hydrological modeling can be challenging due to significant data requirements and computational complexities. Hydrological models must be sufficiently complex to describe physical processes yet simple enough to use. This paper describes the development of a simplified watershed-scale input–output model to simulate runoff quantity and quality during a storm event. This work builds upon an existing semi-distributed rainfall–runoff model by adding calculations for pollutant concentrations based on simplified mass balance equations. The model was tested against various watershed examples of increasing complexity. The results show the change in peak flow and pollutant concentration in different areas of the watershed, demonstrating the model’s ability to account for the dynamics of runoff movement through the watershed. This paper advances watershed management by addressing data scarcity through the development of a simplified hydrological model that effectively incorporates spatial variability within a watershed while requiring minimal data input.

Multi-Fidelity Physics-Informed Generative Adversarial Network for Solving Partial Differential Equations

Abstract We propose a novel method for solving partial differential equations using multi-fidelity physics-informed generative adversarial networks. Our approach incorporates physics supervision into the adversarial optimization process to guide the learning of the generator and discriminator models. The generator has two components: one that approximates the low-fidelity response of the input and another that combines the input and low-fidelity response to generate an approximation of high-fidelity responses. The discriminator identifies whether the input–output pairs accord not only with the actual high-fidelity response distribution, but also with physics. The effectiveness of the proposed method is demonstrated through numerical examples and compared to existing methods.

Inversion Method for Transformer Winding Hot Spot Temperature Based on Gated Recurrent Unit and Self-Attention and Temperature Lag.

The hot spot temperature of transformer windings is an important indicator for measuring insulation performance, and its accurate inversion is crucial to ensure the timely and accurate fault prediction of transformers. However, existing studies mostly directly input obtained experimental or operational data into networks to construct data-driven models, without considering the lag between temperatures, which may lead to the insufficient accuracy of the inversion model. In this paper, a method for inverting the hot spot temperature of transformer windings based on the SA-GRU model is proposed. Firstly, temperature rise experiments are designed to collect the temperatures of the entire side and top of the transformer tank, top oil temperature, ambient temperature, the cooling inlet and outlet temperatures, and winding hot spot temperature. Secondly, experimental data are integrated, considering the lag of the data, to obtain candidate input feature parameters. Then, a feature selection algorithm based on mutual information (MI) is used to analyze the correlation of the data and construct the optimal feature subset to ensure the maximum information gain. Finally, Self-Attention (SA) is applied to optimize the Gate Recurrent Unit (GRU) network, establishing the GRU-SA model to perceive the potential patterns between output feature parameters and input feature parameters, achieving the precise inversion of the hot spot temperature of the transformer windings. The experimental results show that considering the lag of the data can more accurately invert the hot spot temperature of the windings. The inversion method proposed in this paper can reduce redundant input features, lower the complexity of the model, accurately invert the changing trend of the hot spot temperature, and achieve higher inversion accuracy than other classical models, thereby obtaining better inversion results.