Development Strategy, Optimal Industrial Structure and Economic Growth in Less Developed Countries

In this paper, we develop an endogenous growth model that combines structural change with repeated product improvements. There are two sectors in the present paper, one is traditional sector, and the other is modern sector. The technological progress in the traditional sector takes the form of horizontal innovation based on expanding variety, while the technologies in the modern sector become not only increasingly capital-intensive but also progressively productive over time. The application of the basic model to the less developed economies show that the optimal industrial structure in the less developed countries (LDCs) is endogenously determined by its factor endowments; the firm in the LDCs that enters the capital-intensive, advanced industry in the developed countries (DCs) would be nonviable owing to the relative scarcity of capital in the LDCs factor endowments; whether the industrial structure matches with the factor endowment structure or not is the fundamental cause to explain differences in economic performance among the LDCs.Capital Intensity, Development Strategy, Factor Endowments, Endogenous Growth, Industrial Structure, productivity, technology, Viability

Industrial structure, appropriate technology and economic growth in less developed countries

The authors develop an endogenous growth model that combines structural change with repeated product improvement. That is, the technologies in one sector of the model become not only increasingly capital-intensive, but also progressively productive over time. Application of the basic model to less developed economies shows that the (optimal) industrial structure and the (most) appropriate technologies in less developed economies are endogenously determined by their factor endowments. A firm in a less developed country that enters a capital-intensive, advanced industry in a developed country would be nonviable owing to the relative scarcity of capital in the factor endowments of less developed countries.Economic Theory&Research,Political Economy,Technology Industry,Economic Growth,Inequality

Development Strategy, Viability, and Economic Institutions: The Case of China

development strategy, institution, viability, trinity system

Clothoid Curve-based Emergency-Stopping Path Planning with Adaptive Potential Field for Autonomous Vehicles

The Potential Field (PF)-based path planning method is widely adopted for autonomous vehicles (AVs) due to its real-time efficiency and simplicity. PF often creates a rigid road boundary, and while this ensures that the ego vehicle consistently operates within the confines of the road, it also brings a lurking peril in emergency scenarios. If nearby vehicles suddenly switch lanes, the AV has to veer off and brake to evade a collision, leading to the "blind alley" effect. In such a situation, the vehicle can become trapped or confused by the conflicting forces from the obstacle vehicle PF and road boundary PF, often resulting in indecision or erratic behavior, even crashes. To address the above-mentioned challenges, this research introduces an Emergency-Stopping Path Planning (ESPP) that incorporates an adaptive PF (APF) and a clothoid curve for urgent evasion. First, we design an emergency triggering estimation to detect the "blind alley" problem by analyzing the PF distribution. Second, we regionalize the driving scene to search the optimal breach point on the road PF and the final stopping point for the vehicle by considering the possible motion range of the obstacle. Finally, we use the optimized clothoid curve to fit these calculated points under vehicle dynamics constraints to generate a smooth emergency avoidance path. The proposed ESPP-based APF method was evaluated by conducting the co-simulation between MATLAB/Simulink and CarSim Simulator in a freeway scene. The simulation results reveal that the proposed method shows increased performance in emergency collision avoidance and renders the vehicle safer, in which the duration of wheel slip is 61.9% shorter, and the maximum steering angle amplitude is 76.9% lower than other potential field-based methods.Comment: 14 pages, 20 figures, journal paper in submissio

The Game Theory: Applications in the Wireless Networks

Recent years have witnessed a lot of applications in the computer science, especially in the area of the wireless networks. The applications can be divided into the following two main categories: applications in the network performance and those in the energy efficiency. The game theory is widely used to regulate the behavior of the users; therefore, the cooperation among the nodes can be achieved and the network performance can be improved when the game theory is utilized. On the other hand, the game theory is also adopted to control the media access control protocol or routing protocol; therefore, the energy exhaust owing to the data collision and long route can be reduced and the energy efficiency can be improved greatly. In this chapter, the applications in the network performance and the energy efficiency are reviewed. The state of the art in the applications of the game theory in wireless networks is pointed out. Finally, the future research direction of the game theory in the energy harvesting wireless sensor network is presented

Convergence, financial development, and policy analysis

We study the relationship among inflation, economic growth, and financial development in a Schumpeterian overlapping generations model with credit constraints. In the baseline case, money is super-neutral. When the financial development exceeds some critical level, the economy catches up and then converges to the growth rate of the world technology frontier. Otherwise, the economy converges to a poverty trap with a growth rate lower than the frontier and with inflation decreasing with the level of financial development. We then study efficient allocation and identify the sources of inefficiency in a market equilibrium. We show that a particular combination of monetary and fiscal policies can make a market equilibrium attain the efficient allocation.First author draf

Estimating global cropland production from 1961 to 2010

Global cropland net primary production (NPP) has tripled over the last 50 years, contributing 17–45 % to the increase in global atmospheric CO2 seasonal amplitude. Although many regional-scale comparisons have been made between statistical data and modeling results, long-term national comparisons across global croplands are scarce due to the lack of detailed spatiotemporal management data. Here, we conducted a simulation study of global cropland NPP from 1961 to 2010 using a process-based model called Vegetation–Global Atmosphere–Soil (VEGAS) and compared the results with Food and Agriculture Organization of the United Nations (FAO) statistical data on both continental and country scales. According to the FAO data, the global cropland NPP was 1.3, 1.8, 2.2, 2.6, 3.0, and 3.6 PgC yr−1 in the 1960s, 1970s, 1980s, 1990s, 2000s, and 2010s, respectively. The VEGAS model captured these major trends on global and continental scales. The NPP increased most notably in the US Midwest, western Europe, and the North China Plain and increased modestly in Africa and Oceania. However, significant biases remained in some regions such as Africa and Oceania, especially in temporal evolution. This finding is not surprising as VEGAS is the first global carbon cycle model with full parameterization representing the Green Revolution. To improve model performance for different major regions, we modified the default values of management intensity associated with the agricultural Green Revolution differences across various regions to better match the FAO statistical data at the continental level and for selected countries. Across all the selected countries, the updated results reduced the RMSE from 19.0 to 10.5 TgC yr−1 (∼  45 % decrease). The results suggest that these regional differences in model parameterization are due to differences in socioeconomic development. To better explain the past changes and predict the future trends, it is important to calibrate key parameters on regional scales and develop data sets for land management history

Understanding Daily Travel Patterns of Subway Users – An Example from the Beijing Subway

The daily travel patterns (DTPs) present short-term and timely characteristics of the users’ travel behaviour, and they are helpful for subway planners to better understand the travel choices and regularity of subway users (SUs) in details. While several well-known subway travel patterns have been detected, such as commuting modes and shopping modes, specific features of many patterns are still confused or omitted. Now, based on the automatic fare collection (AFC) system, a data-mining procedure to recognize DTPs of all SUs has become possible and effective. In this study, DTPs are identified by the station sequences (SSs), which are modelled from smart card transaction data of the AFC system. The data-mining procedure is applied to a large weekly sample from the Beijing Subway to understand DTPs. The results show that more than 93% SUs of the Beijing Subway travel in 7 DTPs, which are remarkably stable in share and distribution. Different DTPs have their own unique characteristics in terms of time distribution, activity duration and repeatability, which provide a wealth of information to calibrate different types of users and characterize their travel patterns.</p