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The development and evolution of stakeholder collaborative innovation in megaprojects is impacted by various influencing factors. The effect of influencing factors on collaborative innovation performance (CIP) in megaprojects is not a simplistic linear relationship but an iterative and non-linear relationship that requires a dynamic perspective to analyze. Therefore, this paper adopts the system dynamic (SD) approach to investigate the dynamic and interactive relationships between the CIP and the influencing factors.

The study first develops a research framework with the system boundary of “CIP system – organizational collaboration subsystem – knowledge collaboration subsystem – strategic collaboration subsystem”. Then, the causal relationship model, the stock-flow model, and the mathematical equations were determined based on the literature review and the expert interviews. Finally, five performance improvement scenarios were designed according to the practice context of CIP in megaprojects, and simulations were performed using the Vensim PLE software to investigate the CIP from a dynamic perspective.

The findings reveal that the effect of different influencing factors on CIP grows non-linearly, with the cumulative effect becoming more pronounced as time advances. The incentive mechanism has the most significant effect, and the combined effect of multiple influencing factors has a highly significant facilitating effect on improving CIP. Strategic collaboration, organizational collaboration and knowledge collaboration are mutually conditional and reinforcing with each other, which ultimately promotes the improvement of CIP.

This study uncovers the inherent pattern and the interactive dynamic mechanism of factors for improving CIP in the context of megaprojects. It enriches the theoretical research in the area of collaborative innovation in megaprojects and provides practical management strategies for improving CIP.

This study seeks to explore the presence and the relative strength of market efficiency in the onshore and offshore Renminbi (RMB) forward markets.

In the onshore and offshore foreign exchange markets, the RMB forward contracts are designed in similar ways. However, the underlying economic forces and regulatory frameworks are very different in these two markets. We first analyze the functioning of each market, by examining the covered interest rate parity (CIRP) conditions. Second, we explore the CIRP deviations in the two markets and quantify the role of market frictions and government interventions.

We find that the CIRP condition does not hold in either the onshore or the offshore RMB forward markets. We also find that the offshore market is more efficient than the onshore market in conveying private information about investors’ expectation.

Our results reveal that the onshore RMB forward market provides an imperfect platform for investors to manage their currency exposures. We suggest that by opening the offshore market to domestic participants and the onshore market to more foreigners, the forward rates may become more informative with a greater investor mix. These liberalization efforts are important steps in the right directions to improve market efficiency in the Chinese FOREX market.

We review the state of the literature concerning work–family conflict in the military, focusing on service members’ parenting roles and overall family and child well-being. This includes recognition that for many women service members, parenting considerations often arise long before a child is born, thereby further complicating work–family conflict considerations in regard to gender-specific conflict factors such as pregnancy, childbirth, postpartum, and breastfeeding. Subsequently, we consider more gender-invariant conflict factors, such as the nature of the work itself as causing conflict for the service member as parent (e.g., nontraditional hours, long separations, and child care challenges) as well as for the child (e.g., irregular contact with parent, fear for parent’s safety, and frequent relocations), and the ramifications of such conflict on service member and child well-being. Finally, we review formalized support resources that are in place to mitigate negative effects of such conflict, and make recommendations to facilitate progress in research and practice moving forward.

The purpose of this paper is to examine how policy instability is priced in interest rates. Policy instability refers to the likelihood that the current policy will be changed in the future in the absence of political power shifts.

Chinese government’s experimental policy-making approach provides an ideal set of frequent policy flip-flops which allows us to identify the effect of policy changes.

Conditional on the bureaucratic quality of policymaking, a good-quality policy reversal is related to reductions in interest rate term spread and volatility; a bad-quality policy reversal is related to increases in the spread and volatility. The bureaucratic quality is multi-dimensional and the moderating effect is stronger on interest rates when it is measured more precisely.

First, we can use the interest rate dynamics to infer the policy risk premium, which is a more objective market indicator of the bureaucratic quality of the policy change. Second, the study is among the first that documents the pricing of policy instability can be moderated by the bureaucratic quality. The results indicate that it is important for a government to be responsive and consistent in liberalizing the financial market. It will lead to reduced cost of capital and volatility for investors and firms in the economy. Third, given that the bureaucratic quality is multi-dimensional and produces stronger impact jointly, a country shall continue to improve on different aspects of the bureaucratic quality. Although the study is based on the empirical evidence from Chinese policy environment, the results can be broadly applied to any developing economies that intend to liberalize the market to spur economic growth.

Exploring the intrinsic connection between the ecological environment and the digital economy and empirically testing how the level of digital economic development affects the ecological environment. Using the entropy weighting method to analyze the weights of the indicators in the digital economic development level and ecological environment system to explore the factors that have the greatest impact on the ecological environment in the indicator system of the digital economic development level so as to deepen the theoretical understanding of the relationship between the level of development of the digital economy and the ecological environment. Explore the regional heterogeneity of the level of development of the digital economy to promote the healthy development of China’s ecological environment proving the difference in the level of development of the digital economy in the east west and central regions of China and the difference in the effect on the ecological environment.

Based on the panel data of 30 provinces in China from 2013 to 2021 this paper fits the index system of digital economy development level with three factors. A digital infrastructure digital industry and digital application combines environmental pollution and energy consumption to construct ecological environment indicators and explored the impact of digital economy development level on the ecological environment by using the entropy weight method and the random effect model.

The findings indicate that the degree of digital economic development has a positive and significant impact on promoting the healthy development of the ecological environment, in which the digital industry has the greatest impact on the ecological environment. Meanwhile, the improvement of industrial structure also has a positive effect on the improvement of the ecological environment, whereas the level of human capital inhibits the healthy development of the ecological environment, and the governmental support fails to effectively and significantly promote the improvement of the ecological environment. Furthermore, the empirical research indicates that the level of digital economy development has obvious regional heterogeneity on the healthy development of the ecological environment: the eastern and central regions have a significant effect, while the western region has a less significant effect.

Although domestic and foreign scholars and experts have conducted sufficient studies on the ecological environment and the development level of digital economy respectively, there are few studies on the empirical analysis of the positive significance and regional heterogeneity of the impact of the development level of digital economy on the ecological environment, which can be supplemented and referred to in this study. At the same time, it also provides intellectual support for our country to achieve high-quality development of digital economy and efficient governance of ecological environment.

The purpose of this paper is to examine the effects of energy technological innovation on carbon emissions in China from 2001 to 2016.

Conditional mean (CM) methods are first applied to implement our investigation. Then, considering the tremendous heterogeneity in China, quantile regression is further employed to comprehensively investigate the potential heterogeneous effect between energy technological innovation and carbon emission intensity.

The results suggest that renewable energy technological innovation has a significantly positive effect on carbon emission intensity in lower quantile areas and a negative effect in higher quantile areas. Contrarily, fossil energy technological innovation exerts a negative correlation with carbon emission intensity in lower quantile areas and a positive effect on carbon emission intensity in higher quantiles areas.

Considering that energy consumption is the main source of CO₂ emissions, it is of great importance to study the impact of energy technological innovation on carbon emissions. However, the previous studies mainly focus on the impact of integrated technological innovation on carbon emissions, ignoring the impact of energy technological innovation on carbon emissions mitigation. To fill this gap, we construct an extended STIRPAT model to examine the effects of renewable energy technological innovation and fossil energy technological innovation on carbon emissions in this paper. The results can provide a reference for the government to formulate carbon mitigation policies.

This study empirically investigates the impacts of technological innovation and operational efficiency on environmental performance and the moderating effects of environmental orientation.

We develop a conceptual framework based on the Porter Hypothesis. We collect a sample of 850 listed firms in China between 2010 and 2019. The fixed effect model was used to analyse the data.

The empirical findings reveal that technological innovation indirectly enhances environmental performance through operational efficiency and partially mediates this impact. We also find that environmental orientation strengthens the positive impacts of technological innovation and operational efficiency on environmental performance.

This study contributes to the literature by revealing that technological innovation is positively associated with operational efficiency and environmental performance, which suggests that technological innovation can simultaneously enhance business and environmental performance. Hence, this study provides empirical support for the Porter Hypothesis. The results also extend the Porter Hypothesis by revealing how technological innovation affects environmental performance and under what conditions technological innovation has a greater impact on environmental performance.

This paper aims to explore the impact of green finance on the heterogeneity of enterprise green technology innovation and the underlying mechanism between them.

Using the data of China's A-share listed enterprises from 2008 to 2020 and the fixed effect model, the authors empirically explore the relationship and mechanism between green finance and green technology innovation by constructing the green finance index while considering both the quality and quantity of innovation.

The study suggests that green finance is positively related to the quality and quantity of enterprise green technology innovation, while green finance is more effective in stimulating the quality of green technology innovation than quantity. In addition, alleviating financial mismatch and improving the quality of environmental information disclosure are core mechanisms during the process of green finance facilitating green technology innovation. Furthermore, green finance exerts a more positive effect on the quality and quantity of green technology innovation with large-size enterprises, heavily polluting industries and enterprises in the eastern region.

This paper enriches the literature on green finance and green technology innovation and provides practical significance for green finance implementation.

While implementing green innovation-driven strategies when facing growing grim environmental problems and the realistic demands of achieving high-quality development is increasingly urgent, changing abruptly is inevitably detrimental to the smooth functioning of social and economic development. Restrained by resources, innovation-driven strategy is a huge strategy for an organization to shift from traditional technological innovation to green innovation. Supports and implementation in green technology investment would necessarily crowd out other business investment and lead to reduction of innovation outputs and mount of financial uncertainty. Under the guidance of harmonious balance, the equilibrium allocation between green research and non-green counterpart is badly needed to be addressed for decision-makers inside and outside the organizations. The differentiated inputs of them would lead to different effects on organizational performance in practice.

The authors first conducted a Hausman test on green research intensity (GRI) and innovation performance, economic performance, social performance, and environmental performance, respectively. Adopting the fixed effects model for estimation seems accurate, if there is no significant heteroscedasticity shown in the BP test. The authors then adopted the least square dummy variable method to handle individual heterogeneity (Xia et al., 2020). After controlling the industry effect and time effect simultaneously, the results were consistent with that of fixed effects model, thereby eliminating the impact of heteroscedasticity.

The authors construct a multi-dimensional performance system—innovation performance, economic performance, social performance, and environmental performance—to probe into the influence of GRI from the resource-based view and allocation theory. Different performance does not benefit equally from increasing the intensity of green research. Performance increase may squeeze out the quantity of total innovation but can compensate quality for knowledge spillovers of green technology. The organization's growth and long-term value may be beneficial from the increase, but not the short-term financial performance. While the relationship between GRI and social performance has the characteristic of reverse U-curve, there has to be some scale of green research to gain considerable and nonlinear environmental performance. Low level of green research may increase pollution until green research has cross over the inflection point. These relationships are intensely moderated by the environmental regulation.

Because of the focus of this study is on the organizational performance of green research, the analysis comes with some limitations that should be addressed in future research. Data were inter-professional, with large enterprises and small businesses innovating green technology at the same time. Though the hypotheses presented here were grounded in existing theoretical rationale, the generality of this study cannot be assumed. Multi-performance of green activities in small- and medium-sized businesses should be further explored. Additionally, concrete index of the corresponding evaluation system constructed here contribute more to practical activities of green innovation. Refinement of synergy performance index is the task for future work. Further, grounded in Chinese context, the authors' results could be compared with other scenario with institutional heterogeneity to provide detailed evidences for institutional theory. Future studies could also move forward to longitudinal case study to delicately investigate the performance differentiation of green research when in different development stage.

First, what and how the authors do is novel as the authors use listed Chinese manufacturing companies to probe into the complex relationship between GRI and multiple performance rather than discussing the performance of green innovation input from a single perspective merely. Second, the authors systematically define the performance as economic performance, environmental performance, social performance and innovation performance in depth, which consider adequately the tangible and intangible value as well as internal and external benefits of green research. And finally, in the context of environmental regulation, the study discusses the differentiation of the increase of green research intensity from the perspective of resource constraints, providing reference for optimizing the resource allocation in green and non-green research and solving the decoupling between earnest social appeal and sluggish or reluctant green behaviors.