Factors Influencing the Allocation of Regional Sci-Tech Financial Resources Based on the Multiple Regression Model

Abstract

This article uses a multiple regression model to evaluate the extent to which financial resources in various regions can be put into the most appropriate direction through the financial system or financial market. This paper uses the Tobit model to conduct empirical analysis on the input and output data of technology finance in multiple provinces and cities and explores the impact of various factors on technology finance efficiency from the perspective of three technology finance entities: high-tech enterprises, governments, and venture capital companies. Based on the DEA model and Malmquist index model in the data envelopment analysis method, this paper uses deap software to calculate the efficiency of agricultural science and technology resource allocation in Gansu Province and the efficiency of agricultural science and technology operation in many provinces in the western region. This paper divides the allocation of scientific and technological innovation resources into the ability to allocate scientific and technological innovation resources and the allocation efficiency of scientific and technological innovation resources. By constructing a performance evaluation system for the allocation of scientific and technological innovation resources, it is found that there are obvious differences in the ability and efficiency of scientific and technological innovation resource allocation in various regions. This paper proposes the selected efficiency evaluation method, the multiple regression model, which uses the data from 2012 to 2018 to evaluate the efficiency of the allocation of scientific and technological financial resources in my country, calculates the efficiency value of each region, and conducts a comparative analysis of provinces and regions. Research shows that, through multiple regression model analysis, the total technical efficiency of technology finance in Northeast China rose from 0.759 fluctuations in 2012 to 0.922 in 2018, gradually reducing the difference with the eastern and western regions. The overall promotion plays a significant role. The innovation of this article lies in the use of multiple regression models to analyze the factors affecting the allocation of regional scientific and technological financial resources, aiming to improve the efficiency of financial resource allocation.

Optimization Analysis for Innovative Inputs under the Objective Discrepancy between Government and Enterprise

“…However, there was market failure in the R&D activities led by enterprises, and the national economic development needs to be guided by government policies. Under such backgrounds, on the basis of inheriting innovation theory, National System of Innovation, represented by Christopher Freeman and Richard R. Nelson, proposed that technological innovation was not only enterprises activities but also be driven by the national innovation system, which afected the allocation and efciency of innovation resources [8,9].…”

Can the Financial Industry ‘Anchor’ Carbon Emission Reductions?— The Mediating and Moderating Effects of the Technology Market

“…As the primary productive forces, science and technology not only need financial industry support 19 but can also react to industry developments. 23 The deepening of technology market development means that technological transformation is accelerated. As the results of technological innovation are mostly invested in meeting practical needs, financial demands become diversified, 66 which expands the financial industry development scale 67 and guides financial industry funds to carbon emission reduction.…”

“…22 Technology can also react to the financial industry's development. 23 Scientific and technological achievements can guide the rational allocation of funds, 24 control risks, 11 and steer the financial industry's development toward carbon emission reduction. To achieve the goal of “carbon peaking” and “carbon neutral”, the financial industry has been helping to reduce carbon emissions through various means.…”

Data-Driven Green Development Efficiency of Regional Sci-Tech Finance: A Case Study of the Yangtze River Delta

“…Qi et al measured the allocation efficiency of scientific and technological financial resources in Hubei Province and found that the problem of low allocation efficiency of scientific and technological financial resources in Hubei Province is the low value of pure technical efficiency and small effect on scale efficiency [34]. Empirical research shows that the efficiency level of sci-tech finance is affected by the government and its policies, venture capital companies [35], human capital [36], infrastructure environment, education funds [37], the proportion of direct financing, and the incubation capacity of science and technology business incubators [38]. Da Fonseca, RS pointed out that the governments continue to play a critical and determining role in science, technology, and innovation financing whether through financial incentives, fiscal incentives, or a mix of both.…”