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Transitioning to a low-carbon economy will require significant investment to transform energy systems, alter the built environment and adapt infrastructure. A strategy to finance this investment is needed if the limit of a 2°C increase in global mean temperatures is to be respected. Also, high-income countries have pledged to pay the "agreed full incremental costs" of climate-change mitigation by developing countries, which are not necessarily the same as incremental investment costs. Building on simulations using Integrated Assessment Models and historical evidence, this paper explores some of the issues posed by this dual financing challenge. We discuss the "fiscal self-reliance" of the energy sector, finding that carbon pricing would generate sufficient fiscal revenues within each region to finance total investment in energy supply. Even when allowing for trade in emission permits, regional carbon fiscal revenues should still suffice to cover both their own investment in energy supply and permit purchases from abroad. We show that incremental energy-supply investment (and saving) needs are well within the range of past variation of aggregate investment, and argue that the challenge is rather to ensure that revenues from carbon pricing and other sources are complemented by investment in the appropriate sectors. But fairness and equity are likely to warrant transfers from advanced industrial countries to developing nations.

The objective of this paper is to analyze the concept and determinants of "sovereign risk" and the role of the credit risk rating agencies which serve internationally as the main reference instruments employed by economic agents to assess this risk. The paper also tries to identify macroeconomic variables which could be associated with sovereign risk ratings awarded by rating agencies to each country. After examining the indicators on an individual basis, their potential as a group is tested econometrically as a determinant of the class of sovereign risk into which national economies fall. Our results constitute a set of indicators which emerging economies would be well advised to improve upon.

This study compares the sensitivity of each household nonperforming loans (NPLs) category in Malaysia, allowing asymmetry across different household credit types, credit cards, personal uses, purchase of residential properties and purchase of transport vehicles. Differences in the impact of household debt on the Malaysian household NPLs are found evident. In the sample period from 2006 to 2018, the findings suggest an asymmetric impact of credit card debts on the household NPLs. This linkage is explained by only short-run negative changes in credit card outstanding loans. Besides that, the residential property loans behave asymmetrically in the long and short run, with higher impact sources from the negative changes. In a disaggregated NPL analysis focusing on a different type of loan portfolio, the asymmetry further enhances policy and regulation-making in managing household credit risk.

In order to effectively improve the accuracy of Consumer Price Index (CPI) prediction so as to more truly reflect the overall level of the country’s macroeconomic situation, a CPI big data prediction method based on wavelet twin support vector machine (SVM) is proposed. First, the historical CPI data are decomposed into high-frequency part and low-frequency part by wavelet transform. Then a more advanced twin SVM is used to build a prediction model to obtain two kinds of prediction results. Finally, the wavelet reconstruction method is used to fuse the two kinds of prediction results to obtain the final CPI prediction results. The wavelet twin SVM model is used to fit and predict CPI index. Experimental results show that compared with the similar prediction methods, the proposed prediction method has higher fitting accuracy and smaller root mean square error.

The global financial crisis of 2008 challenges some relevant aspects of macroeconomic theory such as the neutrality of money. This paper shows that this neutrality is based on the unrealistic assumption of perfect competition. Relaxing this alone (without time lags, price rigidities, menu costs and other frictions) makes money no longer necessarily neutral and hence makes financial crises and institutions much more important. The presence of increasing returns to scale at the firm level and to specialization at the economy level due to the division of labor also makes finance much more important than suggested by traditional economics.

A macroeconomic model based on the economic variables (i) assets, (ii) leverage (defined as debt over asset) and (iii) trust (defined as the maximum sustainable leverage) is proposed to investigate the role of credit in the dynamics of economic growth, and how credit may be associated with both economic performance and confidence. Our first notable finding is the mechanism of reward/penalty associated with patience, as quantified by the return on assets. In regular economies where the EBITA/Assets ratio is larger than the cost of debt, starting with a trust higher than leverage results in the highest long-term return on assets (which can be seen as a proxy for economic growth). Therefore, patient economies that first build trust and then increase leverage are positively rewarded. Our second main finding concerns a recommendation for the reaction of a central bank to an external shock that affects negatively the economic growth. We find that late policy intervention in the model economy results in the highest long-term return on assets. However, this comes at the cost of suffering longer from the crisis until the intervention occurs. The phenomenon that late intervention is most effective to attain a high long-term return on assets can be ascribed to the fact that postponing intervention allows trust to increase first, and it is most effective to intervene when trust is high. These results are derived from two fundamental assumptions underlying our model: (a) trust tends to increase when it is above leverage; (b) economic agents learn optimally to adjust debt for a given level of trust and amount of assets. Using a Markov Switching Model for the EBITA/Assets ratio, we have successfully calibrated our model to the empirical data of the return on equity of the EURO STOXX 50 for the time period 2000–2013. We find that dynamics of leverage and trust can be highly nonmonotonous with curved trajectories, as a result of the nonlinear coupling between the variables. This has an important implication for policy makers, suggesting that simple linear forecasting can be deceiving in some regimes and may lead to inappropriate policy decisions.

Based on the notions of bipolar lattices and L-sets, YinYang bipolar fuzzy sets and fuzzy equilibrium relations are presented for bipolar clustering, optimization, and global regulation. While a bipolar L-set is defined as a bipolar equilibrium function L that maps a bipolar object set X over an arbitrary bipolar lattice B as L:X ⇒ B, this work focuses on the unit square lattice B_F = [-1, 0] × [0, 1]. A strong or weak bipolar fuzzy equilibrium relation in a bipolar set X is then defined as a reflexive, symmetric, and bipolar interactive (or transitive) fuzzy relation μ_R: X ⇒ B_F. Three types of bipolar α-level sets are presented for bipolar defuzzification and depolarization. It is shown that a fuzzy equilibrium relation is a non-linear bipolar generalization and/or fusion of multiple similarity relations, which induces disjoint or joint bipolar fuzzy subsets including quasi-coalition, conflict, and harmony sets. Equilibrium energy and stability analysis can then be utilized on different clusters for optimization and global regulation purposes. Thus, this work provides a unified approach to truth, fuzziness, and polarity and leads to a holistic theory for cognitive-map-based visualization, optimization, decision, global regulation, and coordination. Basic concepts are illustrated with a simulation in macroeconomics.