An Intelligent Computing Approach to Evaluating the Contribution Rate of Talent on Economic Growth

Abstract

We set out in this study to develop an intelligent computing method for the evaluation of the 'economic contribution rate of talent' (ECRT). We begin by constructing an indicator system for comprehensive evaluation of the talent environment and then go on to classify our (country or region) target system using our proposed GA-DE-FCM methodology. We subsequently identify total 'human capital' as comprising of 'talent capital' and 'general labor', which, along with 'fixed assets', are used as the input variables of the economic system, whilst the corresponding gross domestic product is used as the output variable. The mapping between the inputs and output is modeled in this study by a 'fuzzy artificial neural network' from which several fuzzy rules can be extracted. Having extracted these fuzzy rules, we subsequently go on to investigate the effect of each input factor (fixed assets, talent capital and general labor) on the level of economic growth within each category (obtained in Step 1), and then carry out an examination of the ECRT within each category, as well as that within the whole target system. The traditional methods of evaluating ECRT are not regarded as satisfactory, given that the ECRT problem is non-linear and involves lags; however, we argue that based upon intelligent computing, the model proposed here can effectively deal with these issues. The results, based upon a 2001-2010 sample of 31 provinces in mainland China, indicate that during this period, China could be classified into three categories according to the talent environment. The first category (high level talent environment) comprises of just two regions, with an average ECRT of 44.61 per cent, whilst the second category (median level talent environment) comprises of five B Nuo Liao 123 Y. He et al. regions, with an average ECRT of 37.57 per cent, and the third category (low level talent environment) comprises of 24 regions, with an average ECRT of 14.8 per cent. The average ECRT for China as a whole is 25.67 per cent.

A Grey CES Production Function Model and Its Application in Calculating the Contribution Rate of Economic Growth Factors

“…In the application of the CES production function model, an important aspect is calculating the contribution rates of influencing factors on economic growth. He et al [18] set out to develop an intelligent computing method for the evaluation of the "economic contribution rate of talent" (ECRT). Liu and Lai [19] used the modified CES production function model to (1) construct the production function of China's civil aviation transportation, (2) calculate the contribution rate of the key factors that affect the production capacity of China's civil aviation transportation, and (3) analyze and put forward relevant theoretical suggestions.…”

Analysis on the Classification and Evaluation System of Talents in Colleges and Universities from the Perspective of AHP

“…e existence of these problems is crucial to further deepen the reform of talent evaluation mechanisms [10][11][12]. As a large country of human resources, although China has unique advantages in the development and training of human resources, colleges and universities are gathered, scientific research institutes are numerous, and the industrial economy is booming, there are still various drawbacks in the evaluation of all kinds of talents; In terms of talent resource evaluation and use, the concepts and methods are not advanced enough, and there are many areas that need to be improved [13][14][15][16]. In the new era, China's demand for independent innovation is more urgent than ever before.…”

Analysis on the Influence of China’s Energy Consumption on Economic Growth

“…The second method is multiplier analysis, which uses the lagged variable regression model to calculate four energy consumption's current multiplier, dynamic multiplier and long-term multiplier for economic growth and then compares the influences of four kinds of energy consumption on economic growth in terms of marginal effect [38][39][40][41]. The third method is contribution rate analysis, which calculates the rates of contribution of four energy consumption to economic growth by using the production function model and then compares the influences of four kinds of energy consumption on economic growth in terms of input and output [42][43][44][45][46].…”