The impact of network topological structures on systematic technology adoption and carbon emission reduction

This paper investigates how the topological structure of the technological spillover network among agents affects the adoption of a new clean technology and the reduction of system’s carbon emissions. Through building a systematic technology adoption model with technological spillover effect among agents from the network perspective, this paper first illustrates how the new technology diffuses from the earlier adopters to the later adopters under different network topological structures. Further, this paper examines how the carbon emission constraints imposed on pilot agents affect the carbon emissions of other agents and the entire system under different network topological structures. Simulation results of our study suggest that, (1) different topological structures of the technological spillover network have great influence on the adoption and diffusion of a new advanced technology; (2) imposing carbon emission constraints on pilot agents can reduce carbon emissions of other agents and thereby the entire system. However, the effectiveness of the carbon emission constraints is also largely determined by the network topological structures. Our study implies that the empirical research of the network topological structure among the participating entities is a pre-requisite to evaluate the real effectiveness of a carbon emission reduction policy from the system perspective.

Trust, Incomplete Contracting, and Corporate Innovation

Innovation is a contract-intensive economic activity in a world of incomplete contracts. We show that trust mitigates incomplete contracting and enhances innovation by acting as an informal contracting mechanism. Trust plays an especially important role when formal laws and regulations are lacking, and it promotes innovation by encouraging collaboration and fostering tolerance for failure. Further analyses show that trust also facilitates cross-border technological spillover and innovation collaboration. Overall, our evidence highlights innovation as a key conduit through which trust affects economic growth. This paper was accepted by Gustavo Manso, finance.

Modelling Technological Progress Evaluation: Case of Lithuanian Manufacturing Industry

The goal for this research is to build a framework for analysis of technological spillover effect between sectors in Lithuanian manufacturing industry and assess whether predictors of the created model closely follow dynamic fluctuations of technological progress assessed values. Analysis of academic literature suggested using Granger causality test and vector autoregression (VAR) model to analyze intersectoral technological progress spillover effect in any manufacturing industry. Granger causality test can suggest a potential relationship between technological progress values of particular sectors in manufacturing industry while VAR model can define the exact form and extent of spillover effect. VAR models identify presence of intersectoral technological spillover effect in case of 15 out of 18 sectors in Lithuanian manufacturing industry. In case of a few sectors error terms of VAR models are not stationary suggesting that additional exogenous variables need to be included to increase accuracy of estimated coefficients before these models can be used in further analysis. After minor changes presented VAR models can be used for sensitivity analysis analyzing how changes in different sectoral level parameters affect economic development of manufacturing industry as a whole.

Technological Spillovers, Manufacturing Growth and Transboundary Pollution in Case of Pakistan

Role of technological spillover in manufacturing sector growth and climate change is the running debate in the world to avoid the problem of production inefficiency and environmental damages. Environment friendly technological spillover plays pivotal role in manufacturing sector growth which leads to economic growth. In order to investigate the inconclusiveness of the major issues of production inefficiency and climate change in Pakistan the current research was aimed at finding the relationship between technological spillovers, manufacturing growth and climate change. To meet the objectives, the study investigated both short run and long run dynamics by employing Autoregressive Distributive Lagged (ARDL) model. An annual time series data over the period of 1973 to 2017 was collected for comparative analysis of technological spillover performance in manufacturing sector and environmental condition of the country. The results of CUSM test and Bound test validated the existence of long run co-integration relationship among estimated models. The results demonstrated that technological spillover performs significantly positive role in manufacturing growth with less absorptive ability. The empirical analysis proved that technological spillover and imported technology have affirmative role in reducing amount of net Carbon emission over the long run. It is suggested that the firms should adopt innovative technologies and try to improve absorptive capacity while government must opt country specific policies to control negative externalities.

Technology Spillover Effect in China: The Spatiotemporal Evolution and Its Drivers

Under the background of global economic integration, the technology spillover effect is playing a more and more important role in the technological progress of developing countries. In this circumstance, this paper conducted an in-depth analysis on the 12-year spatial-temporal evolution of the international technology spillover effect and its driving determinants in China during the period of 2003 to 2014. Analytical results highlighted that: (1) As a whole, the international technology spillover effect in general has shown an upward trend in China, except in 2008 and 2012, which are observed as deep-V plunge variations. The plunge in 2008 was more dramatic. After 2011, the growth rate of international technology spillover effect from international import trade and foreign direct investment (FDI) respects slowed obviously down. (2) The spatial distribution of the international technology spillover effect from FDI in China transferred from the ribbon-like pattern to the flake-like pattern, while the effect from import trade held steady with little difference in regional spatial distribution. (3) From the drivers, human capital, economic development, trade openness, and institutional factors promoted the technological spillover effect of import trade channels positively, and financial development, human capital, economic development, and institutional factors promoted the technological spillover effect of FDI channels positively.