Expectations for Finnish road infrastructure in semi-automated truck platooning - Project results

Sophistication of Road Infrastructure Management in a Snowy Cold Region —— Development of a Forecasting Method for Winter Road Conditions and Maintenance Support Systems ——

Studies in Regional Science ◽

10.2457/srs.38.1079 ◽

2008 ◽

Vol 38 (4) ◽

pp. 1079-1091

Author(s):

Naoto TAKAHASHI ◽

Roberto TOKUNAGA ◽

Motoki ASANO ◽

Nobuyoshi ISHIKAWA ◽

Shuniti KAWAKAMI

Keyword(s):

Support Systems ◽

Infrastructure Management ◽

Cold Region ◽

Road Infrastructure ◽

Forecasting Method ◽

Winter Road

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Integration of cross asset risk assessment into road infrastructure asset management

IABSE Symposium Report ◽

10.2749/222137815818358196 ◽

2015 ◽

Vol 105 (28) ◽

pp. 1-7

Author(s):

Alfred Weninger-Vycudil ◽

Barbara Brozek ◽

Roland Spielhofer ◽

Chris Britton ◽

Mark Oldfield

Keyword(s):

Risk Assessment ◽

Asset Management ◽

Road Infrastructure ◽

Asset Risk ◽

Infrastructure Asset Management

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PENGARUH INFRASTRUKTUR SECARA SPASIAL TERHADAP KONVERGENSI PERTUMBUHAN EKONOMI DI INDONESIA

INFO ARTHA ◽

10.31092/jia.v1i1.67 ◽

2017 ◽

Vol 1 ◽

pp. 17-28

Author(s):

Anisa Fahmi

Keyword(s):

Economic Growth ◽

Regional Economics ◽

Developed Countries ◽

Road Infrastructure ◽

Long Run ◽

Catch Up ◽

The Developed Countries ◽

Positive Effect ◽

Neoclassical Growth Theory

Motivated by inter-regional disparities condition that occurs persistently, this study examines the Indonesian economy in the long run in order to know whether it tends to converge or diverge. This convergence is based on the Solow Neoclassical growth theory assuming the existence of diminishing returns to capital so that when the developed countries reach steady state conditions, developing countries will continuously grow up to 'catch-up' with developed countries. Based on regional economics perspective, each region can not be treated as a stand-alone unit,therefore, this study also focuses on the influence of spatial dependency and infrastructure. Economical and political situations of a region will influence policy in that region which will also have an impact to the neighboring regions. The estimation results of spatial cross-regressive model using fixed effect method consistently confirmed that the Indonesian economy in the long term will likely converge with a speed of 8.08 percent per year. Other findings are road infrastructure has a positive effect on economic growth and investment and road infrastructure are spatially showed a positive effect on economic growth. In other words, the investment and infrastructure of a region does not only affect the economic growth of that region but also to the economy of the contiguous regions.

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PENGARUH PEMBANGUNAN INFRASTRUKTUR TERHADAP PDRB DI PROVINSI INDONESIA

Wahana ◽

10.35591/wahana.v22i1.146 ◽

2019 ◽

Vol 22 (1) ◽

pp. 15-27

Author(s):

Suripto Suripto ◽

Eva Dwi Lestari

Keyword(s):

Economic Growth ◽

Economic Development ◽

Panel Data ◽

Infrastructure Investment ◽

Water Infrastructure ◽

Infrastructure Development ◽

Road Infrastructure ◽

Health Infrastructure ◽

Spatial Coverage ◽

The Government

Economic growth is one indicator to measure the success of economic development in a country. Economic development is closely related to infrastructure. Infrastructure development will have an impact on economic growth both directly and indirectly. Therefore, the role of the government in determining infrastructure development policies is very important to increase economic growth in Indonesia. The purpose of this study is to determine the effect of infrastructure on economic growth in Indonesia including road infrastructure, electricity infrastructure, investment, water infrastructure, education infrastructure and health infrastructure in Indonesia in 2015-2017.The analytical tool used in this study is panel data regression with the approach of Fixed Effect Model. The spatial coverage of this study is all provinces in Indonesia, namely 34 provinces, with a series of data from 2015 to 2017 with a total of 102 observations. The data used is secondary data obtained from BPS Indonesia.The results of the study show that (1) the road infrastructure variables have a negative and not significant effect on GDRP. (2) electrical infrastructure variables have a negative and not significant effect on GDRP. (3) investment variables have a positive and significant effect on GDRP. (4) water infrastructure variables have a positive and not significant effect on GDRP. (5) educational infrastructure variables have a positive and not significant effect on GDRP. (6) health infrastructure variables have a positive and significant effect on GDRP. Keywords: development, infrastructure, investment, GDRP, panel data

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