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Chiller fault diagnosis plays a crucial role in optimizing energy efficiency within heating, ventilation, and air conditioning (HVAC) systems. The non-stationary nature of chiller fault data presents a significant challenge, as conventional methodologies often fail to adequately capture the relationships between non-stationary variables. To address this limitation and enhance diagnostic accuracy, this paper proposes an improved graph deviation network for chiller fault diagnosis by integrating the sparse cointegration analysis and the convolutional block attention mechanism. First, in order to obtain sparse fault features in non-stationary operation, this paper adopts the sparse cointegration analysis method (SCA). Further, to augment the diagnosis accuracy, this paper proposes the improved graph deviation network (IGDN) to classify fault datasets, which is a combination of the output of a graph deviation network (GDN) with a convolutional block attention mechanism (CBAM). This novel architecture enables sequential evaluation of attention maps along independent temporal and spatial dimensions, followed by element-wise multiplication with input features for adaptive feature optimization. Finally, detailed experiments and comparisons are performed. Comparative analyses reveal that SCA outperforms alternative feature extraction algorithms in addressing the non-stationary characteristics of chiller systems. Furthermore, the IGDN exhibits superior fault diagnosis accuracy across various fault severity levels.

Renewable energy has become a crucial factor for circular economies, sustainable development, and the environment given the limited non-renewable energy reserves and global environmental degradation mainly resulting from non-renewable energy use. This study explores the effect of educational attainment and economic growth on renewable energy use in a sample of selected emerging markets over the 2000–2018 period through causality and cointegration analyses. Causality analysis points out a significant unidirectional causality from indicators of educational attainment and economic growth to renewable energy use. In other words, educational attainment indicators and economic growth are significant determinants of renewable energy in the short run. Furthermore, the cointegration analysis ascertains a positive influence of indicators of educational attainment and economic growth on renewable energy use in the long run. The findings of both analyses indicate that educational attainment and economic growth have a significant impact on renewable energy use in the short and long term. Therefore, the policies fostering educational attainment can be used as instruments to increase the share of renewable energy use in total energy consumption.

Ekonomik büyüme ile çevresel bozulma ilişkisi literatürde genellikle Çevresel Kuznets Eğrisi (EKC) hipotezi ile araştırılırken son yıllarda malzeme kullanımını merkezine alan Malzeme Kuznets Eğrisi (MKC) hipotezine dayalı çalışmalara da ilginin arttığı görülmektedir. Bu ilginin odağındaki temel gerekçe çevresel bozulmayı ölçmede malzeme kullanımının CO2 emisyonlarına kıyasla daha kapsamlı veri içermesidir. EKC hipotezinden türetilen MKC hipotezi de ekonomik büyüme ile çevresel bozulma arasındaki ilişkinin kuadratik formda olduğunu ve ekonomik büyüme ile malzeme kullanımı arasında önce pozitif daha sonra ise negatif yönlü bir ilişkinin bulunduğunu ileri sürmektedir. Bu çalışmada son yıllarda büyüme performanslarıyla gelişen piyasalar olarak öne çıkan CIVETS ülkelerinde MKC hipotezinin geçerliliğinin test edilmesi amaçlanmaktadır. Bu amaçla diğer çalışmalardan farklı olarak malzeme kullanımını temsilen malzeme ayak izi değişkeni kullanılmıştır. 1975-2019 dönemi için panel eşbütünleşme analizinin gerçekleştirildiği çalışmada elde edilen bulgulara göre i) panel için MKC hipotezi geçerlidir; ii) ülkeler bazında Kolombiya haricinde Endonezya, Güney Afrika, Mısır, Türkiye ve Vietnam'da MKC hipotezi geçerlidir. Ayrıca çalışmada malzeme ayak izini maksimum yapan kişi başı gelir düzeyleri de hesaplanmıştır.

The paper presents empirical evidence on the relationship between current account and fiscal balance for Portugal, Ireland, Italy, Greece, and Spain during the last two decades, by applying a threshold autoregressive methodology initially developed by Enders and Siklos (Journal of Business and Economic Statistics, 19(2), 166–176, 2001) and further developed by Sun (Forest Policy and Economics, 13(6), 479–487, 2011). Our empirical findings show that, first, the relationship between current account and budget balance is characterized by non-linearities, and there is evidence of an asymmetric adjustment within the framework of a long-term cointegration relationship. Second, in the long run, a restrictive fiscal policy is the proper tool for improving the current account balance and the growth prospect for all countries under consideration. Third, in the short run, the adjustment process after a shock takes place, primarily, through the level of the current account, while the budget balance evolves more independently, thus acting as the exogenous variable.

This paper constructs and applies a Composite Economic Diversification Index (CEDIX) to quantify the impact of economic diversification on economic growth in oil-dependent GCC economies during the period 2000–2022. In constructing CEDIX, the export, sectoral, and fiscal dimensions are combined by using principal component analysis. Estimation of the diversification-growth nexus was done through the PMG-ARDL specification. Furthermore, second-generation panel tests, the Pesaran CIPS unit-root test, and Westerlund's ECM cointegration test were implemented, and their results supported the long-run equilibrium of variables. Baseline controls are real Brent oil prices, investment, and labor. Results from the PMG estimation showed that diversification has a positive and statistically significant long-run effect on growth. The speed of adjustment to equilibrium is fast. As would be expected from reform gestation lags, the short-run effects of diversification remain modest, while investment and oil prices are the factors shaping the cyclical movements. A fixed-effects ECM estimated with Driscoll-Kraay standard errors was considered for robustness checking and also supports the sign and significance of our main findings. Therefore, policy should give priority to promoting non-oil exports, sectoral upgrading beyond hydrocarbon markets, and fiscal rebalancing in transforming oil windfalls into durable diversified growth.

The article examines emission intensity drivers in Azerbaijan’s light industry (NACE C13–C15: textiles, apparel, leather) over 2007-2023. Air pollutant intensity is defined as the weight of pollutants per 1 AZN of output (kg/AZN), calibrated where relevant to CO2 intensity. Unit root tests (ADF, Zivot– Andrews) reveal a mix of I(0) and I(1) series, allowing ARDL estimation. Considering shocks from the 2015 exchange rate shift, an ARDL (1,2,1,2,2) with a post-break dummy is estimated. Bounds testing confirms cointegration (F = 12.83). The error correction coefficient (−1.90) indicates rapid adjustment to long-run equilibrium. Long-run results show that currency depreciation raises emission intensity, while sectoral productivity lowers it, consistent with scale effects. Exports have a weak negative impact. In the short run, depreciation raises intensity immediately but partially corrects after one year; output growth mitigates the effect. Model checks (LM, RESET, CUSUM, CUSUMSQ) confirm robustness in a small sample. Policy implications suggest that exchange rate shocks temporarily worsen environmental outcomes, whereas expansion and modernization reduce intensity. Priorities include boosting energy efficiency, strengthening cleaner production, and expanding exports to improve environmental performance.

This paper extends the empirical literature about the effects of fiscal decentralization on the growth of government along three dimensions. It distinguishes between the effects of the level of decentralization from the way local governments finance their expenditures (common pool versus own resources); it uses a panel cointegration approach to separate the long run effects of decentralization from the short run dynamics; and it extends and revises the datasets generally used in these empirical analyses. The results show that the amount of revenue raised by sub-national governments leads to a long-term fall in the size of government but grants have the opposite effect. In addition, a greater decentralization of expenditure leads to greater overall spending. When the short-term is considered these influences work slowly, as the speed of adjustment towards the desired government size is relatively sluggish. In addition, in the short run, there is also a clear effect from the role of local revenue raising powers that stimulates the growth of government. These results appear robust to changes in the composition of the variables, countries and periods included the sample.

Italian health care expenditure (HCE) has been basically explained with two main groups of theories. (1) Those explaining the peculiarity of the HCE growth as depending on demand and supply factors, such as aging population, number of practising physicians per capita, mix of public and private hospitals, number of hospital beds,... (2) Those explaining the growth of total public expenditure as a common feature among industrialized countries, with a huge empirical literature emphasising the role of GDP and/or other structural/institutional variables as the main determinants of HCE across countries. In order to reassess previous findings, we exploit recent results on panel cointegration analysis and test the regional Italian data on HCE and GDP, also taking into account cross-section correlation. The results show that HCE and GDP are cointegrated. The long- and short-term dynamics of HCE are estimated. Our results, providing an empirical support for the existence of Wagner's law, have important policy implications in terms of fiscal sustainability: as income rises, people will choose relatively more HCE. Given the level of the public debt in Italy, any further increases would imply that future government spending may be mainly directed toward debt servicing, likely at the expense of public expenditure on basic infrastructure.

In this paper we develop a comprehensive Vector Autoregression Model consisting of five variables; the stock market and price indices of pairs of countries, as well as their bilateral nominal exchange rate. Then, we show that under certain long-run restrictions, our approach encompasses a large number of specifications encountered in the voluminous literature on testing for capital integration with cointegration techniques. This approach minimizes the risk of accepting the null of no cointegration between the equity price indices because of the introduction of additional stochastic trends through the transformation of those indices on a \"real or nominal US dollar\" basis. Furthermore, other interesting long run specifications emerge either with I(1) only stochastic shocks or with the presence of some I(2) disturbances characterizing the system. We apply the testing methodology on monthly data for the US, UK, Germany, and Japan for the period January 1980-May 2019. The main findings provide partial support in favor of cointegration, and therefore for capital markets integration, among stock market indices when proper attention is given to issues like the identification and temporal stability of the cointegration vectors as well as the choice of units that the stock indices are expressed in.

Economic Sentiment Indicator (ESI) became the most popular composite indicator within the EU with the purpose of monitoring and/or forecasting business cycles in one country or for a region as a whole. Since it is calculated regularly, on a monthly base, and is based on five distinct confidence indicators, the main concern is whether the ESI can be explained and/or can explain the current, past or future values of relevant macroeconomic variables. This implies its relevance in predicting both short- and long-term economic outcomes of, for example, variation in income, unemployment fluctuations, consumption change, inflation modifications, sectoral alterations and etc. The question that arises often in academic, as well as within the EU decision-making circles is whether the ESI be used as an explanatory variable with valuable information for modelling the national output developments. Therefore, the aim of this paper is to reveal the true strength and significance in the ESI-GDP nexus for the EU. Empirical research is based on panel cointegration analysis that utilizes data on the ESI and GDP over the period 2000-2018 for the EU28 countries. The causal relationship between the variables appears to be consistent in the short- and long-run across the panel, suggesting that ESI movements do explain movements in national output, hence can help both private and public sector decision-makers to evaluate their goals and plan their actions.