The Development of an Efficiency-Based Global Green Manufacturing Innovation Index: An Input-Oriented DEA Approach

Innovation-based economic growth is considered to be a vital strategic aim for all economies, but environmentally friendly concepts and sustainable development (SD) must also be considered. The literature on the Global Innovation Index (GII) shows various investigations relevant to innovation, yet the lack of comprehensive consideration within the GII of environmental concerns represents a critical challenge. This paper aims to provide a holistic-perspective evaluation model for the top 15 manufacturing countries worldwide in order to resolve this. The efficiency-based Global Green Manufacturing Innovation Index (GGMII) was developed by formulating an input-oriented data envelopment analysis model. Criteria such as the value added to the gross domestic product (GDP), corresponding CO2 emissions, and unemployment rates were examined in order to represent the economic, environmental, and social dimensions of SD, respectively. Other scientific and technological dimensions were also considered. The data corresponding to all ten of the criteria were collected from World Bank Open Data. The results show a mismatch between the original GII and the proposed GGMII for the top eight manufacturing countries (the United States, the United Kingdom, Germany, Korea, France, China, Japan, and Canada), while the remaining countries (Italy, Spain, Russia, India, Mexico, Brazil, and Indonesia) occupied the same rank in both indices, but showed a sizable diminution in their original GII scores. The proposed GGMII might be utilized as a benchmarking instrument for all countries worldwide in the future.

Introduction

Science and technology have made it possible for people to enjoy longer, richer, healthier lives but also pose existential threats to human survival. Climate change, pandemics, and the danger of nuclear war know no borders and demand a global response. Mixed with politics, economics, business, law, psychology, ethics, security, and culture, science and technology underlie issues of cyberconflict, Internet governance, competitiveness, jobs, inequality, and poverty. Unbridled use of fossil fuels disrupts the climate. Hypersonic missiles, artificial intelligence, and autonomous weapons can lead to the brink of unintended nuclear war. Cyberattacks can cripple critical infrastructure. Safeguards against these dangers have been undermined. The Internet and social media may no longer embody freedom of information, communication, and innovation. Manufacturing innovation creates new, technically demanding jobs. Tackling these issues requires disciplinary knowledge often unfamiliar to international affairs professionals. It requires confronting facts and complexities while the very idea of scientific facts is under attack.

Will Innovation of Pharmaceutical Manufacturing Improve Perceived Health?

By taking 22 OECD countries from 2010 to 2017 as sample, we study the effect of pharmaceutical manufacturing innovation on perceived health by using the panel Tobit model from the entire sample and sub-samples, respectively, as well as analyze their transmission channels by adding moderating effect. Based on the above, we get the following results: first, the pharmaceutical manufacturing innovation 4 years ago has a positive influence on perceived health, which means the improvement of perceived health is closely related to pharmaceutical manufacturing innovation 4 years ago. Second, pharmaceutical manufacturing innovation has a heterogeneous impact on perceived health, which, including the size and direction of the impact effect, is mainly reflected in different pharmaceutical manufacturing innovation levels, population aging degrees, and education levels. Third, income level can positively regulate the impact of pharmaceutical manufacturing innovation on perceived health.

Factors Affecting the Evolution of Advanced Manufacturing Innovation Networks Based on Cloud Computing and Multiagent Simulation

Facing the pressure of low-cost competition brought by the homogenization of commodities, the manufacturing industry seeks to survive by providing services. By providing outsourcing of value-added services to date, we are focusing on innovation in our business model. With the advancement of science and technology, manufacturing innovation is facing higher challenges, especially the popularization of the Internet, which makes the manufacturing industry have to move closer to new industries. Based on cloud computing, this paper conducts a multiagent simulation on the evolution factors of the innovation network of advanced manufacturing. This article takes three types of simulation subjects: evolutionary network, manufacturing (cluster), and innovation evolution system as the research objects. The factors affecting the evolution of the research are innovation resources, innovation opportunities, innovation desire, innovation pressure, relationship strength, network scale, and network scope. Network differences carry over variable indicators and analyze quantitative regression indicators and then build a research model. The research results show that the average conversion efficiency of the manufacturing industry (0.523) is significantly lower than the average R&D innovation efficiency (0.725), which to a certain extent indicates that the manufacturing industry still has weak links in the export conversion stage at the back end of the innovation value chain. Some of the companies may have problems such as low ability to transform scientific and technological achievements and insufficient export competitiveness of high-tech products, which to a large extent affects and restricts the improvement of manufacturing export transformation efficiency.