Suchergebnisse

During the process of international economic integration, the labor issue plays a vital, urgent, and long-term role in the sustainable development of the economy. The impact of employment on a country's investment decisions is significant. The material underpinning of a nation's socio-economic growth is its transport infrastructure. The impact of infrastructure upgrades on employment in Vietnam's economic sectors is the focus of this article. Furthermore, the study investigates whether the Vietnamese government's annual investment in infrastructure development benefits employees as projected (using data from the Vietnam General Statistics Office (VNGSO) for 19 economic sectors from 2005 to 2019). The results of the System Generalized Method of Moments (System-GMM) show that improving the quality of transport infrastructure can significantly increase employment rates in different sectors. The data show that transport infrastructure plays a key role in ensuring smooth connectivity of the entire national, regional and local economies. It reduces transport costs and facilitates the mobility of workers.JEL Classification J8; L91; O18

Abstract
This article uses the Dimensions of Urban Energy Transitions (DUET) framework to analyse energy transitions in two Chinese cities at different development stages and provide insights into the mechanisms underlying decarbonised industrialisation. The results show that a 'green' coalition between industrial actors and local governments is critical to the initiation and scale-up of low-carbon innovations that provide strong endogenous incentives for proactive transitions. The study unveils the relevance of technology-specific characteristics and the potential countering effect of urban politics in shaping the outcomes of energy transitions, adding both nuance and depth to the DUET framework.

AbstractGraphlet counting is a widely explored problem in network analysis and has been successfully applied to a variety of applications in many domains, most notatbly bioinformatics, social science, and infrastructure network studies. Efficiently computing graphlet counts remains challenging due to the combinatorial explosion, where a naive enumeration algorithm needs O(Nk) time fork-node graphlets in a network of sizeN. Recently, many works introduced carefully designed combinatorial and sampling methods with encouraging results. However, the existing methods ignore the fact that graphlet counts and the graph structural information are correlated. They always consider a graph as a new input and repeat the tedious counting procedure on a regular basis even if it is similar or exactly isomorphic to previously studied graphs. This provides an opportunity to speed up the graphlet count estimation procedure by exploiting this correlation via learning methods. In this paper, we raise a novel graphlet count learning (GCL) problem: given a set of historical graphs with known graphlet counts, how to learn to estimate/predict graphlet count for unseen graphs coming from the same (or similar) underlying distribution. We develop a deep learning framework which contains twoconvolutional neural networkmodels and a series of datapreprocessing techniquesto solve the GCL problem. Extensive experiments are conducted on three types of synthetic random graphs and three types of real-world graphs for all 3-, 4-, and 5-node graphlets to demonstrate the accuracy, efficiency, and generalizability of our framework. Compared with state-of-the-art exact/sampling methods, our framework shows great potential, which can offer up to two orders of magnitude speedup on synthetic graphs and achieve on par speed on real-world graphs with competitive accuracy.